Methods for early detection of cancer

ABSTRACT

Disclosed herein are methods, compositions, and devices for use in early detection of cancer. The methods include sequencing a panel of regions in cell-free nucleic acid molecules and detecting one or more tumor markers that are indicative of a cancer.

CROSS-REFERENCE

This International Patent Application claims priority to U.S.Provisional Patent Application Nos. 62/322,773, filed on Apr. 14, 2016,62/322,786, filed on Apr. 14, 2016, 62/324,287, filed on Apr. 18, 2016,62/322,783, filed on Apr. 14, 2016, 62/322,775, filed on Apr. 14, 2016,and 62/322,784, filed on Apr. 14, 2016, each of which is hereinincorporated by reference in its entirety.

BACKGROUND

Cancer is a major cause of disease worldwide. Each year, tens ofmillions of people are diagnosed with cancer around the world, and morethan half of the patients eventually die from it. In many countries,cancer ranks the second most common cause of death followingcardiovascular diseases. Early detection is associated with improvedoutcomes for many cancers.

To detect cancer, several screening tests are available. A physical examand history survey general signs of health, including checking for signsof disease, such as lumps or other unusual physical symptoms. A historyof a patient's health habits and past illnesses and treatments will alsobe taken. Laboratory tests are another type of screening test and mayinclude medical procedures to procure samples of tissue, blood, urine,or other substances in the body before conducting laboratory testing.Imaging procedures screen for cancer by generating visualrepresentations of areas inside the body. Genetic tests detect certaingene deleterious mutations linked to some types of cancer. Genetictesting is particularly useful for a number of diagnostic methods.

SUMMARY

The present disclosure provides methods and systems that may be used forearly cancer detection. Such methods may provide for high sensitivitydetection of one or more genetic variants.

In an aspect, a method comprises (a) providing a sample comprising cfDNAfrom a subject, wherein the subject does not detectably exhibit acancer; (b) capturing from the sample cfDNA molecules covered by asequencing panel, wherein the sequencing panel comprises one or moreregions from each of a plurality of different genes, wherein: (i) thesequencing panel is no greater than 50,000 nucleotides; (ii) thepresence of a tumor marker in any one of the different genes indicatesthat the subject has the cancer; and (iii) at least 80% of subjectshaving the cancer have a tumor marker present in at least one of theplurality of different genes; and (c) sequencing the captured cfDNAmolecules to a read depth sufficient to detect the tumor markers at afrequency in the sample as low as 0.01%.

In some embodiments, the cfDNA is derived from blood, serum or plasma.In some embodiments, the sample comprises between 10 nanograms and 300nanograms cfDNA.

In some embodiments, the cancer is selected from the group consisting ofovarian cancer, pancreatic cancer, breast cancer, colorectal cancer, andnon-small cell lung carcinoma. In some embodiments, the cancer isnon-small cell lung carcinoma, and the non-small cell lung carcinoma issquamous cell carcinoma or adenocarcinoma.

In some embodiments, the subject does not detectably exhibit the canceras shown by one or more imaging methods selected from the groupconsisting of positron emission tomography scan, magnetic resonanceimaging, X-ray, computerized axial tomography scan, and ultrasound. Insome embodiments, the subject has previously undergone treatment for thecancer.

In some embodiments, enriching comprises sequence capture of cfDNAmolecules covered by the panel. In some embodiments, the plurality ofgenes is between 2 to 30 different genes. In some embodiments, theplurality of genes is no more than any of 10, 9, 8, 7, 6, or 5 differentgenes. In some embodiments, the panel comprises a plurality of genesselected from the group consisting of AKT1, ALK, APC, ATM, BRAF, CTNNB1,EGFR, ERBB2, ESR1, FGFR2, GATA3, GNAS, IDH1, IDH2, KIT, KRAS, MET, NRAS,PDGFRA, PIK3CA, PTEN, RB1, SMAD4, STK11, and TP53. In some embodiments,the panel comprises a plurality of genes selected from the groupconsisting of ABL1, AKT1, ALK, APC, AR, ATM, BRAF, CDH1, CDKN2A, CSF1R,CTBBB1, EGFR, ERBB2, ERBB4, EZH2m, FBXW7, FGFR1, FGFR2, FGFR3, FLT3,GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, IDH2, JAK2, JAK3, KDR, KIT, KRAS,MET, MLH1, MPL, MYC, NOTCH1, NPM1, NRAS, PDGFRA, PIK3CA, PTPN11, PTEN,PROC, RB1, RET, SMAD4, SMARCB1, SMO, SRC, STK11, TERT, TP53, and VHL. Insome embodiments, the sequencing panel is about 15,000 nucleotides toabout 30,000 nucleotides.

In some embodiments, a tumor marker is selected from the groupconsisting of a single base substitution, a copy number variation, anindel, a gene fusion, a transversion, a translocation, an inversion, adeletion, aneuploidy, partial aneuploidy, polyploidy, chromosomalinstability, chromosomal structure alterations, chromosome fusions, agene truncation, a gene amplification, a gene duplication, a chromosomallesion, a DNA lesion, abnormal changes in nucleic acid chemicalmodifications, abnormal changes in epigenetic patterns and abnormalchanges in nucleic acid methylation.

In some embodiments, at least 85%, at least 90%, at least 93%, at least95%, at least 97%, at least 98% or at least 99% of subjects having thecancer have a tumor marker present in at least one of the plurality ofdifferent genes.

Some embodiments comprise sequencing the captured cfDNA molecules to aread depth sufficient to detect the tumor markers at a frequency in thesample as low as 0.005%, 0.001% or 0.0005%.

In some embodiments, the one or more regions are selected for the panelto detect one or more differentially methylated regions. In someembodiments, the one or more regions comprise sequences differentiallytranscribed across one or more tissues of the subject. In someembodiments, the panel is selected to detect the one or more tumormarkers with a theoretical sensitivity of 85% or greater. In someembodiments, the panel is selected to achieve a sensitivity of 85% orgreater for one or more cancers selected from the group consisting ofcolorectal cancer, ovarian cancer, lung cancer, and pancreatic cancer.

In some embodiments, assaying the cell-free nucleic acid moleculescomprises subjecting the cell-free nucleic acid molecules to sequencingin the one or more regions in the panel to generate sequence reads.

In some embodiments, sequencing is performed at a read depth of at least1000, at least 5000 at least 10,000, at least 20,000, at least 30,000,at least 50,000, at least 75,000, at least 100,000 unique reads perbase. In some embodiments, subjecting to sequencing comprises sequencingfrom about 1.2 billion to about 6.5 billion base pairs.

In some embodiments, one or more of the cell-free nucleic acid moleculesare isolated from one or more exosomes in the biological sample. In someembodiments, one or more of the cell-free nucleic acid molecules areisolated from one or more cell surface bound nucleic acids.

In some embodiments, the cell-free nucleic acid molecules comprise RNA.In some embodiments, the cell-free nucleic acid molecules comprise DNA.In some embodiments, the cell-free nucleic acid molecules comprisemethylated DNA.

In some embodiments comprise selecting the panel based on nucleosomebinding patterns. In some embodiments, a nucleosome binding pattern isdetermined based on size or number of cfNA (e.g., cfDNA) fragments, forexample, mapping to particular genomic regions.

In some embodiments, one or more regions comprise one or more sequencesselected from the group consisting of exons, introns, promoters, 3′untranslated regions, 5′ untranslated regions, and splice sites. In someembodiments, the one or more tumor markers is detected at a sensitivityof about 80% or greater. In some embodiments, the one or more tumormarkers is detected at a specificity of about 95% or greater. In someembodiments, the one or more tumor markers is detected at a sensitivityof about 80% or greater and a specificity of about 95% or greater. Insome embodiments, the one or more tumor markers is detected at anaccuracy of about 95% or greater.

In an aspect, disclosed herein is a method comprising: a. providing asample comprising cell-free nucleic acid (cfNA) molecules from asubject, wherein the subject does not detectably exhibit a cancer; b.capturing from the sample cfNA molecules covered by a sequencing panel,wherein the sequencing panel comprises one or more regions from each ofa plurality of different genes, wherein: i. the sequencing panel is nogreater than 50,000 nucleotides; ii. a presence of a tumor marker in anyone of the different genes indicates that the subject has the cancer;and iii. at least 80% of subjects having the cancer have a tumor markerpresent in at least one of the plurality of different genes; and c.sequencing the captured cfNA molecules to a read depth sufficient todetect the tumor markers at a frequency in the sample as low as 1.0%,0.75%, 0.5%, 0.25%, 0.1%, 0.075%, 0.05%, 0.025%, 0.01%, or 0.005%.

In some embodiments, the cfNA molecules are derived from blood, serum,or plasma. In some embodiments, the sample comprises between 10nanograms and 300 nanograms of cfNA, and wherein the cfNA is circulatingcell-free DNA (cfDNA).

In some embodiments, the cancer is selected from the group consisting ofovarian cancer, pancreatic cancer, breast cancer, colorectal cancer, andnon-small cell lung carcinoma (NSCLC), or a combination thereof. In someembodiments, the cancer is non-small cell lung carcinoma (NSCLC), andthe non-small cell lung carcinoma (NSCLC) is squamous cell carcinoma oradenocarcinoma. In some embodiments, the subject does not detectablyexhibit the cancer as shown by one or more imaging methods selected fromthe group consisting of positron emission tomography scan, magneticresonance imaging, X-ray, computerized axial tomography scan, andultrasound. In some embodiments, the subject has previously undergonetreatment for the cancer. In some embodiments, enriching comprisessequence capture of cfNA molecules covered by the panel.

In some embodiments, the plurality of genes is between 2 to 30 differentgenes. In some embodiments, the plurality of genes is no more than anyof 10, 9, 8, 7, 6, or 5 different genes. In some embodiments, the panelcomprises a plurality of genes selected from the group consisting ofAKT1, ALK, APC, ATM, BRAF, CTNNB1, EGFR, ERBB2, ESR1, FGFR2, GATA3,GNAS, IDH1, IDH2, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, PTEN, RB1,SMAD4, STK11, and TP53. In some embodiments, the sequencing panel isabout 15,000 nucleotides to about 30,000 nucleotides.

In some embodiments, the tumor marker is selected from the groupconsisting of a single base substitution, an insertion or deletion(indel), a gene fusion, a transversion, a translocation, an inversion, adeletion, aneuploidy, partial aneuploidy, polyploidy, chromosomalinstability, chromosomal structure alterations, chromosome fusions, agene truncation, a gene amplification, a gene duplication, a chromosomallesion, a DNA lesion, abnormal changes in nucleic acid chemicalmodifications, abnormal changes in epigenetic patterns and abnormalchanges in nucleic acid methylation.

In some embodiments, at least 85%, at least 90%, at least 93%, at least95%, at least 97%, at least 98%, or at least 99% of subjects having thecancer have a tumor marker present in at least one of the plurality ofdifferent genes. In some embodiments, the method comprises sequencingthe captured cfNA molecules to a read depth sufficient to detect thetumor markers at a frequency in the sample as low as 0.005%, 0.001%, or0.0005%.

In some embodiments, the one or more regions are selected for thesequencing panel to detect one or more differentially methylatedregions. In some embodiments, the one or more regions comprise sequencesdifferentially transcribed across one or more tissues of the subject. Insome embodiments, the sequencing panel is selected to detect the one ormore tumor markers with a theoretical sensitivity of 85% or greater. Insome embodiments, the panel is selected to achieve a sensitivity of 85%or greater for one or more cancers selected from the group consisting ofcolorectal cancer, ovarian cancer, lung cancer, and pancreatic cancer.

In some embodiments, assaying the cfNA molecules comprises subjectingthe cfNA molecules to sequencing in the one or more regions in thesequencing panel to generate sequence reads. In some embodiments,sequencing is performed at a read depth of at least 1000, at least 5000,at least 10,000, at least 20,000, at least 30,000, at least 50,000, atleast 75,000, or at least 100,000 unique reads per base. In someembodiments, the subjecting to sequencing comprises sequencing fromabout 1.2 billion to about 6.5 billion nucleotides.

In some embodiments, one or more of the cfNA molecules are isolated fromone or more exosomes in the biological sample. In some embodiments, oneor more of the cfNA molecules are isolated from one or more cell surfacebound nucleic acids. In some embodiments, the cfNA molecules compriseRNA. In some embodiments, the cfNA molecules comprise DNA. In someembodiments, the cfNA molecules comprise methylated DNA.

In some embodiments, the method further comprises selecting the panelbased on nucleosome binding patterns. In some embodiments, the one ormore regions comprise one or more sequences selected from the groupconsisting of exons, introns, promoters, 3′ untranslated regions, 5′untranslated regions, and splice sites.

In some embodiments, the cancer is detected at a sensitivity of about80% or greater. In some embodiments, the cancer is detected at aspecificity of about 95% or greater. In some embodiments, the cancer isdetected at a sensitivity of about 80% or greater and a specificity ofabout 95% or greater. In some embodiments, the cancer is detected at anaccuracy of about 95% or greater.

In some embodiments, the cfNA molecules are uniquely tagged with respectto one another. In some embodiments, the cfNA molecules are non-uniquelytagged with respect to one another. In some embodiments, the cfNAmolecules are not tagged.

In another aspect, disclosed herein is a method for detecting cancer ina subject comprising: sequencing circulating cell-free DNA (cfDNA) fromthe subject at a depth of at least 50,000 reads per base to detect oneor more genetic variants associated with cancer. In some embodiments,the sequencing is at a depth of at least 100,000 reads per base. In someembodiments, the sequencing is at a depth of about 120,000 reads perbase. In some embodiments, the sequencing is at a depth of about 150,000reads per base. In some embodiments, the sequencing is at a depth ofabout 200,000 reads per base.

In some embodiments, the reads per base represent at least 5,000original nucleic acid molecules, at least 10,000 original nucleic acidmolecules, at least 20,000 original nucleic acid molecules, at least30,000 original nucleic acid molecules, at least 40,000 original nucleicacid molecules, or at least 50,000 original nucleic acid molecules.

In some embodiments, the one or more genetic variants associated withcancer are selected from the group consisting of an SNV, CNV, indel,fusion, or nucleosome binding pattern. In some embodiments, the SNV isdetected in a gene selected from the group consisting of AKT1, ALK, APC,ATM, BRAF, CTNNB1, EGFR, ERBB2, ESR1, FGFR2, GATA3, GNAS, IDH1, IDH2,KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, PTEN, RB1, SMAD4, STK11, and TP53.In some embodiments, the nucleosome binding pattern is determined basedon size or number of cfDNA fragments.

In some embodiments, the sequencing is performing on an enriched set ofcfDNA molecules. In some embodiments, the enriched set of cfDNAmolecules are representative of less than 60,000 bp across the humangenome. In some embodiments, the enriched set of cfDNA molecules arerepresentative of less than 35,000 bp across the human genome. In someembodiments, the enriched set of cfDNA molecules are representative of10,000-30,000 bp across the human genome. In some embodiments, theenriched set of cfDNA molecules are representative of nucleosome regionsassociated with cancer. In some embodiments, the enriched set of cfDNAmolecules comprises one or more genes selected from the group consistingof: AKT1, ALK, APC, ATM, BRAF, CTNNB1, EGFR, ERBB2, ASR1, FGFR2, GATA3,GNAS, IDH1, IDH2, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, PTEN, RB1, RB1,SMAD4, STK11, and TP53. In some embodiments, the enriched set of cfDNAmolecules comprises one or more enhancer sequences or promotersequences. In some embodiments, the enriched set of cfDNA moleculescomprises one or more genomic loci, and further wherein at least 80% ofsubjects having the cancer have a tumor marker present in at least oneof the one or more genomic loci. In some embodiments, the cancer iscolorectal cancer, ovarian cancer, lung cancer, pancreatic cancer, orliver cancer.

In some embodiments, the method further comprises comparing sequenceinformation from the cfDNA to sequence information obtained from acohort of healthy individuals, a cohort of cancer patients, or germlineDNA from the subject. In some embodiments, the germline DNA from thesubject is obtained from leukocytes from the subject. In someembodiments, the cohort of cancer patients has the same stage of cancer,the same type of cancer, or both. In some embodiments, the cohort ofhealthy individuals may be chosen for certain risk factors such asdemographic risk factors or lifestyle risk factors (e.g., smoking).

In some embodiments, the method further comprises amplifying the cfDNAprior to sequencing, and determining a consensus sequence from sequencereads obtained from the sequencing to reduce errors from amplificationor sequencing. In some embodiments, determining the consensus sequenceis performed on a molecule-by-molecule basis. In some embodiments,determining the consensus sequence is performed on a base by base basis.In some embodiments, detection of consensus sequence is based onassessing probabilities of each of the potential nucleotides based onthe observed sequencing output, as well as sequencing and amplificationerror profile characteristics of an individual sample, a batch ofsamples, or a reference set of samples. In some embodiments, determiningthe consensus sequence is performed using molecular barcodes that tagindividual cfDNA molecules derived from the subject. In someembodiments, a set of molecules with a consensus sequence deviant fromthe human reference is compared to those observed in other samplesprocessed in the laboratory to determine and exclude any potentialcontaminating event. In some embodiments, determining the consensussequence is optimized by comparing the consensus sequence to thoseobtained from the cohort of healthy individuals, the cohort of cancerpatients, or the germline DNA from the subject.

In some embodiments, the method further comprises tagging the cfDNAmolecules with a barcode such that at least 20% of the cfDNA in a samplederived from the subject are tagged. In some embodiments, the tagging isperformed by attaching adaptors comprising a barcode. In someembodiments, the adaptors comprise any or all of blunt end adaptors,restriction enzyme overhang adaptors, or adaptors with a singlenucleotide overhang. In some embodiments, the adaptors with a singlenucleotide overhang comprise C-tail adaptors, A-tail adaptors, T-tailadaptors, and/or G-tail adaptors. In some embodiments, the tagging isperformed by PCR amplification using primers with barcodes. In someembodiments, the barcode is single stranded. In some embodiments, thebarcode is double stranded.

In some embodiments, the method further comprises dividing the cfDNAinto partitions. In some embodiments, the cfDNA in each partition isuniquely tagged with respect to each other partition. In someembodiments, the cfDNA in each partition is non-uniquely tagged withrespect to each other partition. In some embodiments, the cfDNA in eachpartition is not tagged.

In some embodiments, at least 10 ng of cfDNA is obtained from thesubject. In some embodiments, at least 200 or 300 ng or cfDNA isobtained from the subject. In some embodiments, the cfDNA comprises atleast 4000, at least 5000, at least 7,000, at least 10,000, or at least15,000 unique molecules for every base to be sequenced or analyzed.

In some embodiments, the method further comprises obtaining a sample ofat least 10 mL of blood or plasma from the subject. In some embodiments,the method further comprises performing epigenomic or nucleosomalprofiling analysis of the cfDNA. In some embodiments, the method furthercomprises determining a tissue of origin of the cfDNA.

In some embodiments, the method further comprises performing circularsequencing on the cfDNA or amplification products thereof. In someembodiments, the method further comprises batching cfDNA from two ormore different subjects into a single sequencing instruments at a ratiobased on the amount of cfDNA obtained from each of the differentsubjects.

In another aspect, disclosed herein is a method for detecting a tumor ina subject suspected of having cancer or having cancer, comprising: (a)sequencing cell-free DNA (cfDNA) molecules derived from a cell-free DNA(cfDNA) sample obtained from the subject; (b) analyzing sequence readsderived from the sequencing to identify (i) circulating tumor DNA(ctDNA) among the cfDNA molecules and (ii) one or more driver mutationsin the ctDNA; and (c) using information about the presence, absence, oramount of the one or more driver mutations in the ctDNA molecules toidentify (i) the tumor in the subject and (ii) actions for treatment ofthe tumor to be taken by the subject, wherein the method detects thetumor in the subject with a sensitivity of at least 85%, a specificityof at least 99%, and a diagnostic accuracy of at least 99%.

In some embodiments, the cfDNA sample is derived from a blood sampleobtained from the subject.

In some embodiments, the one or more driver mutations comprises asomatic variant detected at a mutant allele frequency (MAF) of no morethan 0.05%. In some embodiments, the one or more driver mutationscomprises a fusion detected at a mutant allele frequency (MAF) of nomore than 0.1%. In some embodiments, the one or more driver mutationscomprises a driver mutation present in EGFR, KRAS, MET, BRAF, RET, ALK,ERBB2, or ROS1.

In some embodiments, the method further comprises detecting mutationdistributions for each of one or more driver mutations, wherein themutation distribution for each of the one or more driver mutations isdetected with a correlation of at least 0.99 to a mutation distributionof the driver mutation detected in a cohort of the subject by tissuegenotyping. In some embodiments, the one or more driver mutationscomprises a KRAS mutation. In some embodiments, the one or more drivermutations comprises a PIK3CA mutation.

In another aspect, disclosed herein is a method for identifyingtreatment for a subject with non-small cell lung carcinoma (NSCLC),comprising: (a) sequencing cell-free DNA (cfDNA) molecules derived froma cell-free DNA (cfDNA) sample obtained from the subject; (b) analyzingsequence reads derived from the sequencing to identify (i) circulatingtumor DNA (ctDNA) among the cfDNA molecules and (ii) a copy numberamplification (CNA) of the MET gene in the ctDNA with a specificity ofat least 99%; and (c) identifying, based at least on the identified CNAof the MET gene, an anti-MET therapy to be administered to the subjectto treat the NSCLC.

In some embodiments, the cfDNA sample is derived from a blood sampleobtained from the subject.

In some embodiments, the NSCLC comprises a stage III cancer.

In some embodiments, the subject was previously treated with an EGFRtyrosine kinase inhibitor (TKI) treatment prior to (a). In someembodiments, the subject was previously treated with chemotherapy,radiotherapy, or chemoradiotherapy prior to (a).

In some embodiments, the CNA comprises an amplification of at leastabout 20. In some embodiments, the CNA comprises an amplification of atleast about 30. In some embodiments, the CNA comprises an amplificationof at least about 40. In some embodiments, the CNA comprises anamplification of at least about 50.

In some embodiments, the CNA is identified with a sensitivity of atleast 80%. In some embodiments, the CNA is identified with a specificityof at least 99.9%. In some embodiments, the CNA is identified with aspecificity of at least 99.99%. In some embodiments, the CNA isidentified with a specificity of at least 99.999%. In some embodiments,the CNA is identified with a specificity of at least 99.9999%.

In some embodiments, the method further comprises administering theanti-MET therapy to the subject to treat the NSCLC.

In another aspect, disclosed herein is a method for monitoring breastcancer in a subject, comprising: (a) sequencing cell-free DNA (cfDNA)molecules derived from a cell-free DNA (cfDNA) sample obtained from thesubject; and (b) analyzing sequence reads derived from the sequencing toidentify (i) circulating tumor DNA (ctDNA) among the cfDNA molecules and(ii) one or more mutations in the ctDNA from the subject selected from:EGFR, Exon 19 deletion; ERBB2, Exon 20 insertion; TP53, E286K mutation;AR, N706S mutation; ALK, G1137R mutation; MAP2K2, E66K mutation; andTP53, K164E mutation.

In some embodiments, the cfDNA sample is derived from a blood sampleobtained from the subject.

In some embodiments, the breast cancer is metastatic breast cancer. Insome embodiments, the metastatic breast cancer is treatment refractory.

In some embodiments, the subject was previously treated with a tyrosinekinase inhibitor (TKI) therapy prior to (a). In some embodiments, thesubject was previously treated with chemotherapy, radiotherapy, orchemoradiotherapy prior to (a).

In some embodiments, the one or more mutations in the ctDNA from thesubject are identified with a sensitivity of at least 80%. In someembodiments, the one or more mutations in the ctDNA from the subject areidentified with a specificity of at least 99%. In some embodiments, theone or more mutations in the ctDNA from the subject are identified witha specificity of at least 99.9%. In some embodiments, the one or moremutations in the ctDNA from the subject are identified with aspecificity of at least 99.99%. In some embodiments, the one or moremutations in the ctDNA from the subject are identified with aspecificity of at least 99.999%. In some embodiments, the one or moremutations in the ctDNA from the subject are identified with aspecificity of at least 99.9999%.

In some embodiments, the method further comprises identifying, based atleast on the identified one or more mutations in the ctDNA from thesubject, a treatment to be administered to the subject to treat thebreast cancer. In some embodiments, the treatment comprises an anti-HER2monoclonal antibody. In some embodiments, the treatment comprises atyrosine kinase inhibitor (TKI) therapy. In some embodiments, the TKItherapy comprises a dual anti-EGFR/ERBB2 TKI therapy. In someembodiments, the method further comprises administering the treatment tothe subject to treat the breast cancer.

In another aspect, disclosed herein is a method for identifying ERBB2driver mutations in a subject with non-small cell lung cancer (NSCLC),the method comprising: (a) sequencing cell-free DNA (cfDNA) moleculesderived from a cell-free DNA (cfDNA) sample obtained from the subject;(b) analyzing sequence reads derived from the sequencing to identify (i)circulating tumor DNA (ctDNA) among the cfDNA molecules and (ii) theERBB2 driver mutations in the ctDNA from the subject, wherein the ERBB2driver mutations comprise one or more ERBB2 insertions or deletions(indels).

In some embodiments, the ERBB2 indels are selected from the groupconsisting of: p.Ala775 Gly776insTyrValMetAla (YVMA), p.Gly776delinsValCys, p.Pro780 Tyr781insGlySerPro, p.Ala775Gly776insSerValMetAla, p.Ala775 Gly776insValAlaAla, p.Glu812Arg814delinsGly, p. Gly776delinsLeuCys, p.Arg756 Glu757delinsLys, andp.Leu755 Glu757delinsProGln.

In some embodiments, the cfDNA sample is derived from a blood sampleobtained from the subject.

In some embodiments, the NSCLC is immunohistochemistry (IHC) negativefor HER2 overexpression.

In some embodiments, the subject was previously treated with a tyrosinekinase inhibitor (TKI) therapy prior to (a). In some embodiments, thesubject was previously treated with chemotherapy, radiotherapy, orchemoradiotherapy prior to (a).

In some embodiments, the one or more ERBB2 indels in the ctDNA from thesubject comprise a variant detected at a mutant allele frequency (MAF)of no more than 0.05%.

In some embodiments, the one or more ERBB2 indels in the ctDNA from thesubject are identified with a sensitivity of at least 80%. In someembodiments, the one or more ERBB2 indels in the ctDNA from the subjectare identified with a specificity of at least 99%. In some embodiments,the one or more ERBB2 indels in the ctDNA from the subject areidentified with a specificity of at least 99.9%. In some embodiments,the one or more ERBB2 indels in the ctDNA from the subject areidentified with a specificity of at least 99.99%.

In some embodiments, the method further comprises identifying, based atleast on the one or more ERBB2 driver mutations in the ctDNA from thesubject, a treatment to be administered to the subject to treat theNSCLC. In some embodiments, the method further comprises identifying acopy number amplification (CNA) of ERBB2 in the cfDNA sample.

In some embodiments, the method further comprises identifying one ormore ERBB2 single nucleotide variants (SNVs) in the cfDNA sample. Insome embodiments, the one or more ERBB2 SNVs are selected from the groupconsisting of: G309R, S310F/Y, L755P/S/V, E757K/Q, I767M, D769Y,G776I/V, V777L, V8421, and E930D.

In some embodiments, the method further comprises identifying, based atleast on the identified one or more ERBB2 indels, a treatment to beadministered to the subject. In some embodiments, the method furthercomprises administering the treatment to the subject to treat the NSCLC.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.To the extent publications and patents or patent applicationsincorporated by reference contradict the disclosure contained in thespecification, the specification is intended to supersede and/or takeprecedence over any such contradictory material. This applicationincorporates by reference International Patent Application No.PCT/US2013/058061, filed Sep. 4, 2013, International Application PatentNo. PCT/US2014/072383, filed Dec. 24, 2014, International PatentApplication No. PCT/US2014/000048, filed Mar. 15, 2014, U.S. patentapplication Ser. No. 15/254,363, filed Sep. 1, 2016, and U.S. patentapplication Ser. No. 15/426,668, filed Feb. 7, 2017.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the disclosure are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present disclosure will be obtained by reference tothe following detailed description that sets forth illustrativeembodiments, in which the principles of the disclosure are utilized, andthe accompanying drawings (also “fig.” and “FIG.” herein), of which:

FIG. 1 depicts an example of the study design described herein. PRE Op:blood draw immediately before surgery; Tumor: Next-generation sequencing(NGS) on surgically resected tumor; INTRA Op: blood draw immediatelyafter surgical resection of tumor; Follow up: blood draw >1 week aftersurgical resection of tumor.

FIG. 2 depicts an example of the methods described herein.

FIG. 3 depicts a 5-gene panel that reports single nucleotide variants(SNVs) and insertions/deletions (indels) in 5 genes.

FIG. 4 depicts diversity across cell-free DNA (cfDNA) input for plasmasamples. Molecular conversions did not reach saturation over the rangeof cfDNA input amounts.

FIG. 5 shows all reported SNVs from the study of example 1. SNVs withMAF >0.02% are reported. Dash indicates that the variant was notdetected.

FIG. 6 shows detection rates using tumor next generation sequencing oncfDNA with surgically resected tumor as reference.

FIG. 7 shows concordance analysis for SNV results for pre-op blood drawsusing tumor NGS on surgically resected tumor as reference.

FIG. 8 shows key sample preparation values.

FIGS. 9A, 9B, 9C, and 9D depict time courses for four colorectal cancer(CRC) patients. All reported SNVs and insertions/deletions are included.N.D. indicates samples where nothing was detected. Filled pointsindicate calls that are concordant with NGS results from surgicallyresected tumor, while unfilled points indicate discordant calls. FIG. 9Adepicts a Stage II CRC patient time course. FIG. 9B depicts a Stage IICRC patient time course. FIG. 9C depicts a Stage II CRC patient timecourse. FIG. 9D depicts a Stage IV CRC patient time course.

FIG. 10 shows a computer control system that is programmed or otherwiseconfigured to implement methods provided herein.

FIG. 11 shows exemplary data demonstrating the correlation of ctDNAconcentrations and tumor volumes.

FIG. 12 shows exemplary oncoprints of four major cancer types:colorectal adenocarcinoma, pancreatic adenocarcinoma, lungadenocarcinoma, and ovarian serous cystadenocarcinoma corresponding to asubset of genes on the 25-gene panel in Example 2.

FIG. 13 depicts an example of the study design described in Example2.Pre Op: blood draw immediately before surgery; Tumor: NGS onsurgically resected tumor; Intra Op: blood draw immediately aftersurgical resection of tumor; Follow up: blood draw >1 week aftersurgical resection of tumor.

FIGS. 14A, 14B, 14C and 14D show a time courses for four patients. Allreported tumor-positive SNVs and insertions/deletions were included.FIGS. 14A and 14B demonstrate successful removal of cancer tissue bysurgery. FIGS. 14C and 14D show the evidence of molecular residualdisease.

FIG. 15 shows genes selected for detection of major cancer typeswith >90% theoretical sensitivity. Bolded genes indicate genes withcomplete exon coverage.

FIGS. 16A and 16B show improved diversity and gene coverage for greatersensitivity. FIG. 16A shows diversity across cfDNA input for analyticalsamples. FIG. 16B shows two genes with significant coverage improvementswith assay optimization.

FIG. 17A depicts copy number of various genes in a patient withnon-small cell lung cancer (NSCLC) after treatment with anti-EGFRtherapy.

FIG. 17B depicts computed tomography/positron emission tomography(CT/PET) scans before and after treatment with crizotinib.

FIG. 18 depicts a frequency distribution of secondary resistancemechanisms to anti-EGFR therapy.

FIG. 19 describes an embodiment wherein genetic variations incirculating tumor DNA were monitored upon detection and throughtreatment of cancer.

FIG. 20 depicts functional ERBB2 single nucleotide variants (SNVs).

DETAILED DESCRIPTION

While various embodiments of the disclosure have been shown anddescribed herein, those skilled in the art will understand that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions may occur to those skilled in the art withoutdeparting from the disclosure. It should be understood that variousalternatives to the embodiments of the disclosure described herein maybe employed.

The term “about” and its grammatical equivalents in relation to areference numerical value can include a range of values up to plus orminus 10% from that value. For example, the amount “about 10” caninclude amounts from 9 to 11. The term “about” in relation to areference numerical value can include a range of values plus or minus10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1% from that value.

The term “at least” and its grammatical equivalents in relation to areference numerical value can include the reference numerical value andgreater than that value. For example, the amount “at least 10” caninclude the value 10 and any numerical value above 10, such as 11, 100,and 1,000.

The term “at most” and its grammatical equivalents in relation to areference numerical value can include the reference numerical value andless than that value. For example, the amount “at most 10” can includethe value 10 and any numerical value under 10, such as 9, 8, 5, 1, 0.5,and 0.1.

As used herein the singular forms “a”, “an”, and “the” can includeplural referents unless the context clearly dictates otherwise. Thus,for example, reference to “a cell” can include a plurality of such cellsand reference to “the culture” can include reference to one or morecultures and equivalents thereof known to those skilled in the art, andso forth. All technical and scientific terms used herein can have thesame meaning as commonly understood to one of ordinary skill in the artto which this disclosure belongs unless clearly indicated otherwise.

The term “subject,” as used herein, generally refers to an animal, suchas a mammalian species (e.g., human) or avian (e.g., bird) species, orother organism, such as a plant. More specifically, the subject can be avertebrate, e.g., a mammal such as a mouse, a primate, a simian or ahuman. Animals include, but are not limited to, farm animals, sportanimals, and pets. A subject can be a healthy individual, an individualthat has or is suspected of having a disease or a pre-disposition to thedisease, or an individual that is in need of therapy or suspected ofneeding therapy. A subject can be a patient.

The term “polynucleotide,” as used herein, generally refers to amolecule comprising one or more nucleic acid subunits. A polynucleotidecan include one or more subunits selected from adenosine (A), cytosine(C), guanine (G), thymine (T) and uracil (U), or variants thereof. Anucleotide can include A, C, G, T or U, or variants thereof. Anucleotide (nt) can include any subunit that can be incorporated into agrowing nucleic acid strand. Such subunit can be an A, C, G, T, or U, orany other subunit that is specific to one or more complementary A, C, G,T or U, or complementary to a purine (i.e., A or G, or variant thereof)or a pyrimidine (i.e., C, T or U, or variant thereof). A subunit canenable individual nucleic acid bases or groups of bases (e.g., AA, TA,AT, GC, CG, CT, TC, GT, TG, AC, CA, or uracil-counterparts thereof) tobe resolved. In some examples, a polynucleotide is deoxyribonucleic acid(DNA) or ribonucleic acid (RNA), or variants or derivatives thereof. Apolynucleotide can be single-stranded or double-stranded.

The term “genome” generally refers to an entirety of an organism'shereditary information. A genome can be encoded either in DNA or in RNA.A genome can comprise coding regions that code for proteins as well asnon-coding regions. A genome can include the sequence of all chromosomestogether in an organism. For example, the human genome has a total of 46chromosomes. The sequence of all of these together constitutes a humangenome.

The terms “reference genome” and “reference sequence” as used herein,generally refers a sequence to which an analyzed sequence is compared.In some cases, a reference genome or reference sequence can be includedwith the population of cell-free polynucleotides to be analyzed. Areference genome may be, for example, a nucleic acid with a knownsequence and a known quantity. A reference genome can be of the subjector another individual. A reference genome can be a digital construct,assembled to be representative of a species' set of genes, and stored ona database. The database can be internal or external. A reference genomecan include the genome of any species of interest. Human genomesequences useful as references can include the hg19 assembly or anyprevious or available assembly. Such sequences can be interrogated usingthe genome browser available at genom.ucsc.edu/index.html. Other speciesgenomes include, for example PanTro2 (chimp) and mm9 (mouse).

The term “genetic variant,” as used herein, generally refers to analteration, variant or polymorphism in a nucleic acid sample or genomeof a subject. Such alteration, variant or polymorphism can be withrespect to a reference genome, which may be a reference genome of thesubject or other individual. Single nucleotide polymorphisms (SNPs) area form of polymorphisms. In some examples, one or more polymorphismscomprise one or more single nucleotide variations (SNVs), insertions,deletions, repeats, small insertions, small deletions, small repeats,structural variant junctions, variable length tandem repeats, and/orflanking sequences. Copy number variants (CNVs), transversions and otherrearrangements are also forms of genetic variation. A genomicalternation may be a base change, insertion, deletion, repeat, copynumber variation, transversion, or a combination thereof.

The term “tumor marker”, as used herein refers to a genetic variantassociated with a cancer. A tumor marker and a cancer may be associatedsuch that detection of a tumor marker is indicative of a subject havingthe cancer. A tumor marker may be indicative of a probability that asubject has a cancer. A tumor marker may be a cancer driver mutation. Acancer driver mutation may be a somatic mutation that causes, or“drives”, cancer progression.

The terms “adaptor(s)”, “adapter(s)” and “tag(s)” are used synonymouslythroughout this specification. An adaptor or tag can be coupled to apolynucleotide sequence to be “tagged” by any approach includingligation, hybridization, or other approaches.

The term “library adaptor” or “library adapter” as used herein,generally refers to a molecule (e.g., polynucleotide) whose identity(e.g., sequence) can be used to differentiate polynucleotides in asample (e.g., a biological sample).

The term “sequencing adaptor,” as used herein, generally refers to amolecule (e.g., polynucleotide) that is adapted to permit a sequencinginstrument to sequence a target polynucleotide, such as by interactingwith the target polynucleotide to enable sequencing. The sequencingadaptor permits the target polynucleotide to be sequenced by thesequencing instrument. In an example, the sequencing adaptor comprises anucleotide sequence that hybridizes or binds to a capture polynucleotideattached to a solid support of a sequencing system, such as a flow cell.In another example, the sequencing adaptor comprises a nucleotidesequence that hybridizes or binds to a polynucleotide to generate ahairpin loop, which permits the target polynucleotide to be sequenced bya sequencing system. The sequencing adaptor can include a sequencermotif, which can be a nucleotide sequence that is complementary to aflow cell sequence of other molecule (e.g., polynucleotide) and usableby the sequencing system to sequence the target polynucleotide. Thesequencer motif can also include a primer sequence for use insequencing, such as sequencing by synthesis. The sequencer motif caninclude the sequence(s) needed to couple a library adaptor to asequencing system and sequence the target polynucleotide.

The terms “sensitivity”, “specificity”, and “accuracy” as used hereinrefer to measures of agreement. Sensitivity generally refers to thepercentage of actual positives identified in a test as positive.Sensitivity includes, for example, instances in which one should havefound (diagnosed) a cancer (e.g., by detecting a variant) and did (e.g.,as verified by sampling cellular DNA or tumor tissue). Sensitivity canbe calculated using the following equation: Sensitivity=TP/(TP+FN).Specificity generally refers to the percentage of actual negativesidentified in a test as negative. Specificity includes, for example,instances in which one should have found (diagnosed) no cancer (e.g.,found no variants indicating cancer) and did not (e.g., as verified bysampling cellular DNA or tumor tissue). Specificity can be calculatedusing the following equation: Specificity=TN/(TN+FP). Subjectsidentified as positive in a test that are in reality positive arereferred to as true positives (TP). Subjects identified as positive in atest that are in reality negative are referred to as false positives(FP). Subjects identified as negative in a test that are in realitynegative are referred to as true negatives (TN). Subjects identified asnegative in a test that are in reality positive are referred to as falsenegatives (FN).

Positive predictive value (PPV) can be measured by the percentage ofsubjects who test positive that are true positives. PPV can becalculated using the following equation: PPV=TP/(TP+FP), where TP aretrue positives and FP are false positives.

Negative predictive value (NPV) can be measured by the percentage ofsubjects who test negative that are true negatives. NPV can becalculated using the following equation: NPV=TN/(TN+FN), where TN aretrue negatives and FN are false negatives.

Accuracy can be measured by the percentage of subjects who test positiveor negative that are true positives or true negatives, respectively.Accuracy can be calculated using the following formula:Accuracy=(TP+TN)/(TP+TN+FP+FN).

Precision can be measured by the percentage of subjects who testpositive that are true positives and not false positives. Precision canbe calculated using the following formula: precision=TP/(TP+FP).

I. Overview

With improvements in sequencing and techniques to manipulate nucleicacids, there is a need in the art for improved methods and systems forusing cell free DNA to detect and monitor disease. In particular, thereis a need to balance efficient use of sequenced base pairs with highaccuracy and sensitivity variant detection. Provided herein are methodsfor detecting cancer in a subject. The methods provided herein can beused to detect genetic variation with high sensitivity and accuracy inheterogeneous polynucleotide samples, such as cell-free DNA (“cfDNA”).

An aspect of the present disclosure provides methods for detecting acancer in a subject by detecting one or more genetic variants in a panelof regions of cell-free DNA molecules obtained from a subject. Thepanels of regions can have relatively small sizes, e.g., 50 kilobases(kb) or less. A “region” as described herein may refer to a contiguousportion of a genome. As a non-limiting example, a region may be acontiguous sequence of at least 1000 base pairs (bp). A genomic regioncan be a contiguous segment of at least 1000 nucleotides, at least 2000nucleotides, at least 5000 nucleotides, at least 10,000 nucleotides, atleast 25,000 nucleotides, at least 50,000 nucleotides, at least 100,000nucleotides, at least 250,000 nucleotides, at least 500,000 nucleotides,or at least 1 million nucleotides. A genomic region can include an exon,an intron, a gene, an intergenic regulatory element, gene promoter, genetranscription start site, or a multigene region. The sizes of theregions can allow for deeper sequencing orders of nucleic acid moleculesin a sample on a per base-read basis, which in turn enables detection oflow-frequency genetic variants (e.g., at a minor allele frequency ofabout 0.001% or about 0.01%) in the cell-free DNA sample. Thus, themethods herein can be used for detecting cancers at a high sensitivityand/or a high specificity at a low cost. In some cases, the methods canbe used for detecting a cancer with a sample that has low concentrationof cell-free DNA and/or low-frequency genetic variants, such as a samplefrom an early stage cancer patient. A “locus” as described herein canrefer to a nucleotide, a sequence of nucleotides, or a gene.

Methods herein can also allow for interrogating methylation status andgenetic variants (e.g., SNVs, indels) in cfDNA (double-stranded and/orsingle-stranded), cell-free RNA (cfRNA) (including exosomal RNA) in onesample. Assays herein can be performed with high input amounts (e.g., upto 250 nanograms (ng), up to 300 ng, up to 350 ng, at least 50 ng, atleast 100 ng, at least 150 ng, at least 200 ng, at least 250 ng, atleast 300 ng, at least 350 ng, at least 400 ng, at least 450 ng, atleast 500 ng, at least 550 ng, at least 600 ng, at least 650 ng, or atleast 700 ng) of cell-free nucleic acids without saturation. A cleanupstep (e.g., after end-repair and/or prior to ligation) can be omittedfrom the method, thus preserving unique molecules and shorter fragmentsin the sample. Lower hybridization temperature in the amplification,differential bait concentrations, amplification with primers thatselectively amply GC-rich regions at higher efficiency, and/orsequencing steps can also be used for more uniform coverage acrossguanine-cytosine (GC), and less frequency towards allele imbalance.

An aspect of the present disclosure provides methods for detecting atumor marker or genetic variant among the sequence reads of a genepanel, wherein detection of the tumor marker or genetic variantindicates the presence of cancer. In some embodiments, detection of asingle marker or genetic variant is associated with the presence ofcancer, and detection of a plurality of markers or genetic variantsindicates the presence of cancer.

The methods can comprise one or more of the following steps: a)obtaining cell-free nucleic acid molecules from a sample of the subject;b) selecting a panel of regions from each of a plurality of differentgenes; c) subject the cell-free nucleic acid molecules to sequencing inone or more of the regions; and d) detecting one or more geneticvariants in the sequence read generated from c). The one or more geneticvariants can be indicative of a cancer in the subject.

II. Test Samples

Methods disclosed herein can comprise isolating one or morepolynucleotides.

A polynucleotide can comprise any type of nucleic acid, such as DNAand/or RNA. For example, if a polynucleotide is DNA, it can be genomicDNA, complementary DNA (cDNA), or any other deoxyribonucleic acid. Apolynucleotide can also be a cell-free nucleic acid such as cell-freeDNA (cfDNA). For example, the polynucleotide can be circulating cfDNA.Circulating cfDNA may comprise DNA shed from bodily cells via apoptosisor necrosis. cfDNA shed via apoptosis or necrosis may originate fromnormal bodily cells. Where there is abnormal tissue growth, such as forcancer, tumor DNA may be shed. The circulating cfDNA can comprisecirculating tumor DNA (ctDNA).

A polynucleotide can be double-stranded or single-stranded.Alternatively, a polynucleotide can comprise a combination of adouble-stranded portion and a single-stranded portion. Polynucleotidesdo not have to be cell-free.

A sample can be any biological sample isolated from a subject. Forexample, a sample can comprise, without limitation, bodily fluid, wholeblood, platelets, serum, plasma, stool, red blood cells, white bloodcells or leukocytes, endothelial cells, tissue biopsies, synovial fluid,lymphatic fluid, ascites fluid, interstitial or extracellular fluid, thefluid in spaces between cells, including gingival crevicular fluid, bonemarrow, cerebrospinal fluid, saliva, mucous, sputum, semen, sweat,urine, fluid from nasal brushings, fluid from a pap smear, or any otherbodily fluids. A bodily fluid can include saliva, blood, or serum. Forexample, a polynucleotide can be cell-free DNA isolated from a bodilyfluid, e.g., blood or serum. A sample can also be a tumor sample, whichcan be obtained from a subject by various approaches, including, but notlimited to, venipuncture, excretion, ejaculation, massage, biopsy,needle aspirate, lavage, scraping, surgical incision, or intervention orother approaches. A sample can be a cell-free sample (e.g., notcomprising any cells).

A sample can comprise a volume of plasma containing cell free DNAmolecules. A sample may comprise a volume of plasma sufficient toachieve a given read depth. A volume of sampled plasma may be at least0.5 milliliters (mL), 1 mL, 5 mL 10 mL, 20 mL, 30 mL, or 40 mL. A volumeof sampled plasma at most 0.5 mL, 1 mL, 5 mL 10 mL, 20 mL, 30 mL, or 40mL. A volume of sampled plasma may be 5 to 20 mL. A volume of sampledplasma may be 10 ml to 20 mL.

A sample can comprise various amount of nucleic acid that containsgenome equivalents. For example, a sample of about 30 ng DNA can containabout 10,000 (10⁴) haploid human genome equivalents and, in the case ofcfDNA, about 200 billion (2×10¹¹) individual polynucleotide molecules.Similarly, a sample of about 100 ng of DNA can contain about 30,000haploid human genome equivalents and, in the case of cfDNA, about 600billion individual molecules.

A sample can comprise nucleic acids from different sources. For example,a sample can comprise germline DNA or somatic DNA. A sample can comprisenucleic acids carrying mutations. For example, a sample can comprise DNAcarrying germline mutations and/or somatic mutations. A sample can alsocomprise DNA carrying cancer-associated mutations (e.g.,cancer-associated somatic mutations). In some embodiments, a samplecomprises one or more of: a single base substitution, a copy numbervariation, an indel, a gene fusion, a transversion, a translocation, aninversion, a deletion, aneuploidy, partial aneuploidy, polyploidy,chromosomal instability, chromosomal structure alterations, chromosomefusions, a gene truncation, a gene amplification, a gene duplication, achromosomal lesion, a DNA lesion, abnormal changes in nucleic acidchemical modifications, abnormal changes in epigenetic patterns,abnormal changes in distributions of nucleic acid (e.g., cfDNA)fragments across genomic regions, abnormal changes in distributions ofnucleic acid (e.g., cfDNA) fragment lengths, and abnormal changes innucleic acid methylation.

Methods herein can comprise obtaining certain amount of nucleic acidmolecules, e.g., cell-free nucleic acid molecules from a sample. Forexample, the method can comprise obtaining up to about 600 ng, up toabout 500 ng, up to about 400 ng, up to about 300 ng, up to about 200ng, up to about 100 ng, up to about 50 ng, or up to about 20 ng ofcell-free nucleic acid molecules from a sample. The method can compriseobtaining at least 1 femtogram (fg), at least 10 fg, at least 100 fg, atleast 1 picogram (pg), at least 10 pg, at least 100 pg, at least 1 ng,at least 10 ng, at least 100 ng, at least 150 ng, or at least 200 ng ofcell-free nucleic acid molecules. The method can comprise obtaining atmost 1 femtogram (fg), at most 10 fg, at most 100 fg, at most 1 picogram(pg), at most 10 pg, at most 100 pg, at most 1 ng, at most 10 ng, atmost 100 ng, at most 150 ng, or at most 200 ng of cell-free nucleic acidmolecules. The method can comprise obtaining 1 femtogram (fg) to 200 ng,1 picogram (pg) to 200 ng, 1 ng to 100 ng, 10 ng to 150 ng, 10 ng to 200ng, 10 ng to 300 ng, 10 ng to 400 ng, 10 ng to 500 ng, 10 ng to 600 ng,10 ng to 700 ng, 10 ng to 800 ng, 10 ng to 900 ng, or 10 ng to 1000 ngof cell-free nucleic acid molecules. An amount of cell-free nucleic acidmolecules may be equivalent to a number of haploid genome copies.Because a haploid genome copy has a mass of about 3.3 picograms (pg),each nanogram (ng) of cell-free nucleic molecules may be equivalent toabout 300 haploid genome copies. For example, 5 ng of cell-free nucleicacid molecules may be equivalent to 1,500 genome copies.

A cell-free nucleic acid can be any extracellular nucleic acid that isnot attached to a cell. A cell-free nucleic acid can be a nucleic acidcirculating in blood. Alternatively, a cell-free nucleic acid can be anucleic acid in other bodily fluid disclosed herein, e.g., urine. Acell-free nucleic acid can be a deoxyribonucleic acid (“DNA”), e.g.,genomic DNA, mitochondrial DNA, or a fragment thereof. A cell-freenucleic acid can be a ribonucleic acid (“RNA”), e.g., mRNA,short-interfering RNA (siRNA), microRNA (miRNA), circulating RNA (cRNA),transfer RNA (tRNA), ribosomal RNA (rRNA), small nucleolar RNA (snoRNA),Piwi-interacting RNA (piRNA), long non-coding RNA (long ncRNA), or afragment thereof. In some cases, a cell-free nucleic acid is a DNA/RNAhybrid. A cell-free nucleic acid can be double-stranded,single-stranded, or a hybrid thereof. A cell-free nucleic acid can bereleased into bodily fluid through secretion or cell death processes,e.g., cellular necrosis and apoptosis.

A cell-free nucleic acid can comprise one or more epigeneticallymodifications. For example, a cell-free nucleic acid can be acetylated,methylated, ubiquitylated, phosphorylated, sumoylated, ribosylated,and/or citrullinated. For example, a cell-free nucleic acid can bemethylated cell-free DNA.

Cell-free DNA typically has a size distribution of about 110 to about230 nucletoides, with a mode of about 168 nucleotides. A second, minorpeak detected in assays quantifying cell-free nucleic acid moleculelength has a range between 240 to 440 nucleotides. Additional higherorder nucleotide peaks are present as well at longer lengths.

In some embodiments of the present disclosure, cell-free nucleic acidscan be at most 1,000 nucleotides (nt) in length, at most 500 nucleotidesin length, at most 400 nucleotides in length, at most 300 nucleotides inlength, at most 250 nucleotides in length, at most 225 nucleotides inlength, at most 200 nucleotides in length, at most 190 nucleotides inlength, at most 180 nucleotides in length, at most 170 nucleotides inlength, at most 160 nucleotides in length, at most 150 nucleotides inlength, at most 140 nucleotides in length, at most 130 nucleotides inlength, at most 120 nucleotides in length, at most 110 nucleotides inlength, or at most 100 nucleotides in length.

In some embodiments of the present disclosure, cell-free nucleic acidscan be at least 1,000 nucleotides in length, at least 500 nucleotides inlength, at least 400 nucleotides in length, at least 300 nucleotides inlength, at least 250 nucleotides in length, at least 225 nucleotides inlength, at least 200 nucleotides in length, at least 190 nucleotides inlength, at least 180 nucleotides in length, at least 170 nucleotides inlength, at least 160 nucleotides in length, at least 150 nucleotides inlength, at least 140 nucleotides in length, at least 130 nucleotides inlength, at least 120 nucleotides in length, at least 110 nucleotides inlength, or at least 100 nucleotides in length. Cell-free nucleic acidscan be from 140 to 180 nucleotides in length.

In some embodiments of the present disclosure, cell free nucleic acidsin a subject may derive from a tumor. For example cell-free DNA isolatedfrom a subject can comprise circulating tumor DNA, (ctDNA). Nextgeneration sequencing allows detection and measurement of raremutations. Detection of mutations relative to germline sequence in afraction of cell-free DNA can indicate the presence of ctDNA, thusindicating the presence of a tumor. Sequencing cell free DNA may allowdetection a genetic variant that is known to indicate the presence ofcancer. For example sequencing cell free DNA may allow detection ofmutations in cancer related genes.

Isolation and Extraction

Cell-free polynucleotides may be fetal in origin (via fluid taken from apregnant subject), or may be derived from tissue of the subject itself.Cell-free polynucleotides may derive from healthy tissue, from diseasedtissue such as tumor tissue, or from a transplant organ.

In some embodiments, cell-free polynucleotides are derived from a bloodsample or a fraction thereof. For example, a blood sample (e.g., about10 to about 30 mls) can be taken from a subject, centrifuged to removecells, and the resulting plasma used for cfDNA extraction.

Isolation and extraction of polynucleotides may be performed throughcollection of bodily fluids using a variety of techniques. In somecases, collection may comprise aspiration of a bodily fluid from asubject using a syringe. In other cases collection may comprisepipetting or direct collection of fluid into a collecting vessel.

After collection of bodily fluid, polynucleotides may be isolated andextracted using a variety of techniques utilized in the art. In somecases, cell-free DNA may be isolated, extracted and prepared usingcommercially available kits such as the Qiagen Qiamp® CirculatingNucleic Acid Kit protocol. In other examples, Qiagen Qubit™ dsDNA HSAssay kit protocol, Agilent™ DNA 1000 kit, or TruSeq™ Sequencing LibraryPreparation; Low-Throughput (LT) protocol may be used.

Generally, cell free polynucleotides may be extracted and isolated byfrom bodily fluids through a partitioning step in which cell-free DNAs,as found in solution, are separated from cells and other non-solublecomponents of the bodily fluid. Partitioning may include, but is notlimited to, techniques such as centrifugation or filtration. In othercases, cells may not be partitioned from cell-free DNA first, but ratherlysed. For instance, the genomic DNA of intact cells may be partitionedthrough selective precipitation. Sample partitioning may be combinedwith tagging nucleic acids with identifiers (such as identifierscomprising bar codes), or may be performed in a method without the useof an identifier. A sample can be divided into partitions such that eachpartition can be barcoded independently (e.g., with one unique bar codeper partition), and sequencing data from the partitions can later berecombined. A sample can be divided into partitions, and the nucleicacid molecules non-uniquely tagged with respect to one another within apartition, or between partitions. In some embodiments, a sample can bedivided into partitions without the use of identifiers. In one example,a cfDNA sample is divided into 4 or more partitions, wherein eachpartition is an a spatially addressable location. Sample preparation andsequencing is performed on each spatially addressable partition, and thebioinformatics pipeline utilizes the addressable location to furtheridentify a unique molecule. In one example, nucleic acid molecules canbe divided into partitions, for example, containing different types ofnucleic acid molecules (e.g., double stranded nucleic acids such as DNAand/or single stranded nucleic acids such as RNA and/or single strandedDNA). Cell-free polynucleotides, including DNA, may remain soluble andmay be separated from insoluble genomic DNA and extracted. Generally,after addition of buffers and other wash steps specific to differentkits, DNA may be precipitated using isopropanol precipitation. Furtherclean up steps may be used such as silica based columns or beads (suchas magnetic beads) to remove contaminants or salts. General steps may beoptimized for specific applications. Non-specific bulk carrierpolynucleotides, for example, may be added throughout the reaction tooptimize certain aspects of the procedure such as yield.

In some embodiments, a plasma sample is treated to degrade proteinase Kand DNA is precipitated with isopropanol and subsequently captured on aQiagen column. The DNA then can be eluted (e.g., using 100 microliters(μ1) of eluent such as water or Tris-EDTA (TE) elution buffer). In someembodiments, a portion of the DNA can be selected based on size (e.g.,DNA of 500 nucleotides or fewer in length), for example, using SolidPhase Reversible Immobilization (SPRI) beads, such as AgenCourt® AMPure®beads. In some embodiments, the DNA can be resuspended in a smallervolume, such as 30 μl of water, and checked for size distribution of theDNA (e.g., to check for a major peak at 166 nucleotides and a minor peakat 330 nucleotides). Approximately 5 ng of DNA may be equivalent toabout 1500 haploid genome equivalents (“HGE”).

After extraction, samples may yield up to 1 microgram (μg) of DNA, up to800 ng of DNA, up to 500 ng of DNA, up to 300 ng of DNA, up to 250 ng ofDNA, up to 200 ng of DNA, up to 180 ng of DNA, up to 160 ng of DNA, upto 140 ng of DNA, up to 120 ng of DNA, up to 100 ng of DNA, up to 90 ngof DNA, up to 80 ng of DNA, up to 70 ng of DNA, up to 60 ng of DNA, upto 50 ng of DNA, up to 40 ng of DNA, up to 30 ng of DNA, up to 20 ng ofDNA, up to 10 ng of DNA, up to 9 ng of DNA, up to 8 ng of DNA, up to 7ng of DNA, up to 6 ng of DNA, up to 5 ng of DNA, up to 4 ng of DNA, upto 3 ng of DNA, up to 2 ng of DNA, or up to 1 ng of DNA.

After extraction, samples may yield at least 1 ng of DNA, at least 3 ngof DNA, at least 5 ng of DNA, at least 7 ng of DNA, at least 10 ng ofDNA, at least 20 ng of DNA, at least 30 ng of DNA, at least 40 ng ofDNA, at least 50 ng of DNA, at least 70 ng of DNA, at least 100 ng ofDNA, at least 150 ng of DNA, at least 200 ng of DNA, at least 250 ng ofDNA, at least 300 ng of DNA, at least 400 ng of DNA, at least 500 ng ofDNA, or at least 700 ng of DNA.

One or more of the cell-free nucleic acids can be isolated from acellular fragment in a sample. In some cases, one or more of thecell-free nucleic acids are isolated from membrane, cellular organelles,nucleosomes, exosomes, or nucleus, mitochondria, rough endoplasmicreticulum, ribosomes, smooth endoplasmic reticulum, chloroplasts, Golgiapparatus, Golgi bodies, glycoproteins, glycolipids, cisternaes,liposomes, peroxisomes, glyoxysomes, centriole, cytoskeleton, lysosomes,cilia, flagellum, contractile vacuole, vesicles, nuclear envelopes,vacuoles, microtubule, nucleoli, plasma membrane, endosomes, chromatins,or a combination thereof. One or more of the cell-free nucleic acids canbe isolated from one or more exosomes. In some cases, one or more of thecell-free nucleic acids are isolated from one or more cell surface boundnucleic acids.

Purification of cell free DNA may be accomplished using any methodology,including, but not limited to, the use of commercial kits and protocolsprovided by companies such as Sigma Aldrich, Life Technologies, Promega,Affymetrix, IBI or the like. Kits and protocols may also benon-commercially available.

After isolation, in some cases, the cell free polynucleotides may bepre-mixed with one or more additional materials, such as one or morereagents (e.g., ligase, protease, polymerase) prior to sequencing.

Cell-free DNA can be sequenced at a read depth sufficient to detect agenetic variant at a frequency in a sample as low as 0.0005%. Cell-freeDNA can be sequenced at a read depth sufficient to detect a geneticvariant at a frequency in a sample as low as 0.001%. Cell-free DNA canbe sequenced at a read depth sufficient to detect a genetic variant at afrequency in a sample as low as 1.0%, 0.75%, 0.5%, 0.25%, 0.1%, 0.075%,0.05%, 0.025%, 0.01%, or 0.005%. Thus, sequencing cell free DNA allowsvery sensitive detection of cancer in a subject.

In some embodiments, cellular DNA can be used as an alternative tocell-free DNA. Exemplary cells include, but are not limited to,endothelial cells, cells from tissue biopsies, tumor cells, and cellsfrom whole blood including platelets, red blood cells, and white bloodcells or leukocytes. In some embodiments, sequence data from cellularDNA are obtained at the same or greater read depth than cell-free DNAsequence data.

Methods herein can be used to detect cancer in a subject. Cell free DNAcan be sequenced in subjects not known to have cancer, or suspected ofhaving cancer to diagnose the presence of absence of a cancer.Sequencing cell free DNA provides a noninvasive method for earlydetection of cancer or for ‘biopsy’ of a known cancer. Cell free DNA canbe sequenced in subjects diagnosed with cancer to provide informationabout the cancer. Cell free DNA can be sequenced in subjects before andafter treatment for cancer to determine the efficacy of the treatment.

A subject may be suspected of having cancer or may not be suspected ofhaving cancer. A subject may have experienced symptoms consistent with adiagnosis of cancer. A subject may not have experienced any symptoms, ormay have exhibited symptoms not consistent with cancer. A subject mayhave been diagnosed with a cancer based on biological imaging methods. Asubject may not have a cancer that is detectable by imaging methods. Theimaging methods can be positron emission tomography scan, magneticresonance imaging, X-ray, computerized axial tomography scan,ultrasound, or a combination thereof.

A subject may exhibit a cancer. Alternatively, a subject may notdetectably exhibit a cancer. In some cases, a subject who does notdetectably exhibit a cancer can have a cancer, but have no detectablesymptoms. Subjects not known to have cancer, or suspected of havingcancer, can have cancer that is not detectable using various cancerscreening methods. No cancer may be detected using various imagingmethods. The imaging methods may include, for example, positron emissiontomography scan, magnetic resonance imaging, X-ray, computerized axialtomography scan, endoscopy, ultrasound, or a combination thereof. For asubject not known to have cancer or suspected of having cancer, testssuch as tissue biopsy, bone marrow aspiration, pap tests, fecal occultblood tests, protein biomarker detection, e.g., prostate-specificantigen test, alpha-fetoprotein blood test, or CA-125 test, or acombination thereof, may indicate that a subject does not have cancer,e.g., detect no cancer for the subject. In other cases, a subject whodoes not detectably exhibit a cancer may not have any cancer.

The subject may be at higher risk of having cancer than a generalpopulation. The subject may have a family history of cancer. The subjectmay have known genetic sources of cancer risk. The subject may have beenexposed to environmental conditions known to increase or cause cancerrisk. The subjects can be patients whose only risk factors for cancerare age and/or gender. The subject may have no known cancer riskfactors.

The subject may have been diagnosed with a cancer. The cancer may beearly stage or late stage. The cancer may be metastatic or may not bemetastatic. Types of cancer that a subject may have been diagnosed withinclude, but are not limited to: carcinomas, sarcomas, lymphomas,leukemia's, germ cell tumors and blastomas. Types of cancer that asubject may have been diagnosed with include, but are not limited to:Acute lymphoblastic leukemia (ALL), Acute myeloid leukemia,Adrenocortical carcinoma, adult acute Myeloid leukemia, adult carcinomaof unknown primary site, adult malignant Mesothelioma, AIDS-relatedcancers, AIDS-related lymphoma, Anal cancer, Appendix cancer,Astrocytoma, childhood cerebellar or cerebral, Basal-cell carcinoma,Bile duct cancer, Bladder cancer, Bone tumor, osteosarcoma/malignantfibrous histiocytoma, Brain cancer, Brainstem glioma, Breast cancer,Bronchial adenomas/carcinoids, Burkitt Lymphoma, Carcinoid tumor,Carcinoma of unknown primary, Central nervous system lymphoma,cerebellar astrocytoma, cerebral astrocytoma/malignant glioma, Cervicalcancer, childhood acute Myeloid leukemia, childhood cancer of unknownprimary site, Childhood cancers, childhood cerebral astrocytoma,childhood Mesothelioma, Chondrosarcoma, Chronic lymphocytic leukemia,Chronic myelogenous leukemia, Chronic myeloproliferative disorders,Colon cancer, Cutaneous T-cell lymphoma, Desmoplastic small round celltumor, Endometrial cancer, endometrial Uterine cancer, Ependymoma,Epitheliod Hemangioendothelioma (EHE), Esophageal cancer, Ewing familyof tumors Sarcoma, Ewing's sarcoma in the Ewing family of tumors,Extracranial germ cell tumor, Extragonadal germ cell tumor, Extrahepaticbile duct cancer, Eye cancer, intraocular melanoma, Gallbladder cancer,Gastric (stomach) cancer, Gastric carcinoid, Gastrointestinal carcinoidtumor, Gastrointestinal stromal tumor (GIST), Gestational trophoblastictumor, Glioma of the brain stem, Glioma, Hairy cell leukemia, Head andneck cancer, Heart cancer, Hepatocellular (liver) cancer, Hodgkinlymphoma, Hypopharyngeal cancer, Hypothalamic and visual pathway glioma,Islet cell carcinoma (endocrine pancreas), Kaposi sarcoma, Kidney cancer(renal cell cancer), Laryngeal cancer, Leukaemia, acute lymphoblastic(also called acute lymphocytic leukaemia), Leukaemia, acute myeloid(also called acute myelogenous leukemia), Leukaemia, chronic lymphocytic(also called chronic lymphocytic leukemia), Leukaemias, Leukemia,chronic myelogenous (also called chronic myeloid leukemia), Leukemia,hairy cell, Lip and oral cavity cancer, Liposarcoma, Liver cancer(primary), Lung cancer, non-small cell, Lung cancer, small cell,Lymphoma (AIDS-related), Lymphomas, Macroglobulinemia, Waldenström, Malebreast cancer, Malignant fibrous histiocytoma of bone/osteosarcoma,medulloblastoma, Melanoma, Merkel cell cancer, Metastatic squamous neckcancer with occult primary, Mouth cancer, Multiple endocrine neoplasiasyndrome, childhood, multiple Myeloma (cancer of the bone-marrow),Multiple myeloma/plasma cell neoplasm, Mycosis fungoides,Myelodysplastic syndromes, Myelodysplastic/myeloproliferative diseases,Myelogenous leukemia, chronic, Myxoma, Nasal cavity and paranasal sinuscancer, Nasopharyngeal carcinoma, Neuroblastoma, Non-Hodgkin Lymphomas,Non-small cell lung cancer, Oligodendroglioma, Oral cancer,Oropharyngeal cancer, Osteosarcoma/malignant fibrous histiocytoma ofbone, Ovarian cancer, Ovarian epithelial cancer (surfaceepithelial-stromal tumor), Ovarian germ cell tumor, Ovarian lowmalignant potential tumor, Pancreatic cancer, Pancreatic cancer, isletcell, Paranasal sinus and nasal cavity cancer, Parathyroid cancer,Penile cancer, Pharyngeal cancer, Pheochromocytoma, Pineal astrocytoma,Pineal germinoma, Pineoblastoma and supratentorial primitiveneuroectodermal tumors, Pituitary adenoma, Plasma cellneoplasia/Multiple myeloma, Pleuropulmonary blastoma, Primary centralnervous system lymphoma, Prostate cancer, Rectal cancer, Renal cellcarcinoma (kidney cancer), Renal pelvis and ureter transitional cellcancer, Retinoblastoma, Rhabdomyosarcoma, Salivary gland cancer, Sézarysyndrome, Skin cancer (melanoma), Skin cancer (non-melanoma), Skincarcinoma, Merkel cell, Small cell lung cancer, Small intestine cancer,soft tissue Sarcoma, Squamous cell carcinoma, Squamous neck cancer withoccult primary, metastatic, Stomach cancer, Supratentorial primitiveneuroectodermal tumor, T-Cell lymphoma, cutaneous, Testicular cancer,Throat cancer, Thymoma and thymic carcinoma, Thymoma, Thyroid cancer,Transitional cell cancer of the renal pelvis and ureter, Ureter andrenal pelvis, transitional cell cancer, Urethral cancer, Uterinesarcoma, Vaginal cancer, visual pathway and hypothalamic glioma, Visualpathway and hypothalamic glioma, childhood, Vulvar cancer, Waldenströmmacroglobulinemia, and Wilms tumor (kidney cancer).

The subject may have previously received treatment for a cancer. Thesubject may have received surgical treatment, radiation treatment,chemotherapy, targeted cancer therapeutics or a cancer immunotherapy.The subject may have been treated with a cancer vaccine. The subject mayhave been treated with an experimental cancer treatment. The subject maynot have received a cancer treatment. The subject may be in remissionfrom cancer. The subject may have previously received a treatment forcancer and not detectably exhibit any symptoms.

In some embodiments, the methods and systems described herein can detectcancer before the cancer may be detectable using conventional methods,e.g., at least 1 year, 6 months, 3 months, or 1 month before the cancermay be detectable by imaging, or at least 1 year, 6 months, 3 months, or1 month before the cancer may be diagnosed at stage I, stage II, stageIII, or stage IV, or at least 1 year, 6 months, 3 months, or 1 monthbefore the cancer may recur.

III. Genetic Analysis

Certain DNA sequencing methods use sequence capture to enrich forsequences of interest. Sequence capture typically involves the use ofoligonucleotide probes that hybridize to the sequence of interest. Aprobe set strategy can involve tiling the probes across a region ofinterest. Such probes can be, e.g., about 60 to 120 bases long. The setcan have a depth of about 2×, 3×, 4×, 5×, 6×, 8×, 9×, 10×, 15×, 20×, 50×or more. The effectiveness of sequence capture depends, in part, on thelength of the sequence in the target molecule that is complementary (ornearly complementary) to the sequence of the probe. Enriched nucleicacid molecules can be representative of more than 5,000 bases of thehuman genome, more than 10,000 bases of the human genome, more than15,000 bases of the human genome, more than 20,000 bases of the humangenome, more than 25,000 bases of the human genome, more than 30,000bases of the human genome, more than 35,000 bases of the human genome,more than 40,000 bases of the human genome, more than 45,000 bases ofthe human genome, more than 50,000 bases of the human genome, more than55,000 bases of the human genome, more than 60,000 bases of the humangenome, more than 65,000 bases of the human genome, more than 70,000bases of the human genome, more than 75,000 bases of the human genome,more than 80,000 bases of the human genome, more than 85,000 bases ofthe human genome, more than 90,000 bases of the human genome, more than95,000 bases of the human genome, or more than 100,000 bases of thehuman genome. Enriched nucleic acid molecules can be representative ofno greater than 5,000 bases of the human genome, no greater than 10,000bases of the human genome, no greater than 15,000 bases of the humangenome, no greater than 20,000 bases of the human genome, no greaterthan 25,000 bases of the human genome, no greater than 30,000 bases ofthe human genome, no greater than 35,000 bases of the human genome, nogreater than 40,000 bases of the human genome, no greater than 45,000bases of the human genome, no greater than 50,000 bases of the humangenome, no greater than 55,000 bases of the human genome, no greaterthan 60,000 bases of the human genome, no greater than 65,000 bases ofthe human genome, no greater than 70,000 bases of the human genome, nogreater than 75,000 bases of the human genome, no greater than 80,000bases of the human genome, no greater than 85,000 bases of the humangenome, no greater than 90,000 bases of the human genome, no greaterthan 95,000 bases of the human genome, or no greater than 100,000 basesof the human genome. Enriched nucleic acid molecules can berepresentative of 5,000-100,000 bases of the human genome, 5,000-50,000bases of the human genome, 5,000-30,000 bases of the human genome,10,000-100,000 bases of the human genome, 10,000-50,000 bases of thehuman genome, or 10,000-30,000 bases of the human genome. Enrichednucleic acid molecules can be representative of various nucleic acidfeatures, including genetic variants such as nucleotide variants (SNVs),copy number variants (CNVs), insertions or deletions (e.g., indels),nucleosome regions associated with cancer, gene fusions, and inversions.

Generally, the methods and systems provided herein are useful forpreparation of cell free polynucleotide sequences to a down-streamapplication sequencing reaction. The sequencing method can be massivelyparallel sequencing, that is, simultaneously (or in rapid succession)sequencing any of at least 100, 1000, 10,000, 100,000, 1 million, 10million, 100 million, 1 billion, or 10 billion polynucleotide molecules.Sequencing methods may include, but are not limited to: high-throughputsequencing, pyrosequencing, sequencing-by-synthesis, single-moleculesequencing, nanopore sequencing, semiconductor sequencing,sequencing-by-ligation, sequencing-by-hybridization, RNA-Seq (Illumina),Digital Gene Expression (Helicos), Next generation sequencing, SingleMolecule Sequencing by Synthesis (SMSS) (Helicos), massively-parallelsequencing, Clonal Single Molecule Array (Solexa), shotgun sequencing,Maxam-Gilbert or Sanger sequencing, primer walking, sequencing usingPacBio, SOLiD, Ion Torrent, or Nanopore platforms and any othersequencing methods known in the art.

Individual polynucleotide fragments in a genomic nucleic acid sample(e.g., genomic DNA sample) can be uniquely identified by tagging withnon-unique identifiers, e.g., non-uniquely tagging the individualpolynucleotide fragments.

Sequencing Panel

To improve the likelihood of detecting tumor indicating mutations, theregion of DNA sequenced may comprise a panel of genes or genomicregions. Selection of a limited region for sequencing (e.g., a limitedpanel) can reduce the total sequencing needed (e.g., a total amount ofnucleotides sequenced. A sequencing panel can target a plurality ofdifferent genes or regions to detect a single cancer, a set of cancers,or all cancers.

In some aspects, a panel targets a plurality of different genes orgenomic regions is selected such that a determined proportion ofsubjects having a cancer exhibits a genetic variant or tumor marker inone or more different genes or genomic regions in the panel. The panelmay be selected to limit a region for sequencing to a fixed number ofbase pairs. The panel may be selected to sequence a desired amount ofDNA. The panel may be further selected to achieve a desired sequenceread depth. The panel may be selected to achieve a desired sequence readdepth or sequence read coverage for an amount of sequenced base pairs.The panel may be selected to achieve a theoretical sensitivity, atheoretical specificity and/or a theoretical accuracy for detecting oneor more genetic variants in a sample.

Probes for detecting the panel of regions can include those fordetecting hotspots regions as well as nucleosome-aware probes (e.g.,KRAS codons 12 and 13) and may be designed to optimize capture based onanalysis of cfDNA coverage and fragment size variation impacted bynucleosome binding patterns and GC sequence composition. Regions usedherein can also include non-hotspot regions optimized based onnucleosome positions and GC models. The panel can comprise a pluralityof subpanels, including subpanels for identifying tissue of origin(e.g., use of published literature to define 50-100 baits representinggenes with most diverse transcription profile across tissues (notnecessarily promoters)), whole genome scaffold (e.g., for identifyingultra-conservative genomic content and tiling sparsely acrosschromosomes with handful of probes for copy number base liningpurposes), transcription start site (TSS)/CpG islands (e.g., forcapturing differential methylated regions (e.g., DifferentiallyMethylated Regions (DMRs)) in for example in promoters of tumorsuppressor genes (e.g., SEPT9/VIM in colorectal cancer)). In someembodiments, markers for a tissue of origin are tissue-specificepigenetic markers.

The one or more regions in the panel can comprise one or more loci fromone or a plurality of genes. The plurality of genes may be selected forsequencing and tumor marker detection. Genes included in the region tobe sequenced may be selected from genes known to be involved in cancer,or from genes not involved in cancer. For example the plurality of genesin the panel may be oncogenes, tumor suppressors, growth factors, DNArepair genes, signaling genes, transcription factors, receptors ormetabolic genes. Examples of genes that may be in the panel include, butare not limited to: APC, AR, ARID1A, BRAF, BRCA1, BRCA2, CCND1, CCND2,CCNE1, CDK4, CDK6, CDKN2A, CDKN2B, EGFR, ERBB2, FGFR1, FGFR2, HRAS, KIT,KRAS, MET, MYC, NF1, NRAS, PDGFRA, PIK3CA, PTEN, RAF1, TP53, AKT1, ALK,ARAF, ATM, CDH1, CTNNB1, ESR1, EZH2, FBXW7, FGFR3, GATA3, GNA11, GNAQ,GNAS, HNF1A, IDH1, IDH2, JAK2, JAK3, MAP2K1, MAP2K2, MLH1, MPL, NFE2L2,NOTCH1, NPM1, NTRK1, PTPN11, RET, RHEB, RHOA, RIT1, ROS1, SMAD4, SMO,SRC, STK11, TERT, VHL.

In some cases, the one or more regions in the panel can comprise one ormore loci from one or a plurality of genes, including one or more ofAKT1, ALK, APC, ATM, BRAF, CTNNB1, EGFR, ERBB2, ESR1, FGFR2, GATA3,GNAS, IDH1, IDH2, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA, PTEN, RB1,SMAD4, STK11, and TP53.

In some cases, the one or more regions in a panel for colorectal cancercan comprise one or more loci from one or a plurality of genes,including one of, two of, three of, four of, or five of TP53, APC, BRAF,KRAS, and NRAS. In some cases, the one or more regions in a panel forovarian cancer can comprise one or more loci from one or a plurality ofgenes, including TP53. In some cases, the one or more regions in a panelfor pancreatic cancer can comprise one or more loci from one or aplurality of genes, including one or both of TP53 and KRAS. In somecases, the one or more regions in a panel for lung adenocarcinoma cancomprise one or more loci from one or a plurality of genes, includingone of, two of, three of, four of, five of, six of, seven of, or eightof TP53, BRAF, KRAS, EGFR, ERBB2, MET, STK11, and ALK. In some cases,the one or more regions in a panel for lung squamous cell carcinoma cancomprise one or more loci from one or a plurality of genes, includingone of, two of, three of, four of, or five of TP53, BRAF, KRAS, MET, andALK. In some cases, the one or more regions in a panel for breast cancercan comprise one or more loci from one or a plurality of genes,including one of, two of, three of, or four of TP53, GATA3, PIK3CA, andESR1. In some cases, one or more regions in a panel can comprise one ormore loci from a combination of any of the above genes, for example, todetect a combination of cancer types. In some cases, one or more regionsin a panel can comprise one or more loci from each of the precedinggenes, for example, in a pan-cancer panel.

In some cases, the one or more regions in a panel for lung cancer cancomprise one or more loci from a plurality of genes, including one of,two of, three of, four of, five of, six of, seven of, eight of, nine of,10 of, 11 of, 12 of, 13 of, 14 of, 15 of, 16 of, 17 of, 18 of, 19 of, or20 of EGFR, KRAS, TP53, CDKN2A, STK11, BRAF, PIK3CA, RB1, ERBB2, PTEN,NFE2L2, MET, CTNNB1, NRAS, MUC16, NF1, BAI3, SMARCA4, ATM, NTRK3, andERBB4. Such a panel also may include, or have substituted for any or allof the above, any or all of an EGFR Exon 19 deletion, EGFR L858R, EGFRC797S, EGFR T790M, EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, METexon 14 skipping, SNVs and indels. Many of these genes may be clinicallyactionable, such that an observed anomaly in MAF (e.g., significantlyhigher or lower than in normal control subjects) may be indicative of aclinical state relevant to lung cancer, such as diagnosis, prognosis,risk stratification, treatment selection, tumor resistance to treatment,tumor burden, etc. Such a lung cancer targeted panel may comprise arelatively small number of these lung cancer associated genes.

In some cases, the one or more regions in a panel for breast cancer cancomprise one or more loci from a plurality of genes, including any oneof, or any combination of, ACVRL1, AFF2, AGMO, AGTR2, AHNAK, AHNAK2,AKAP9, AKT1, AKT2, ALK, APC, ARID1A, ARID1B, ARID2, ARID5B, ASXL1,ASXL2, ATR, BAP1, BCAS3, BIRC6, BRAF, BRCA1, BRCA2, BRIP1, CACNA2D3,CASP8, CBFB, CCND3, CDH1, CDKN1B, CDKN2A, CHD1, CHEK2, CLK3, CLRN2,COL12A1, COL22A1, COL6A3, CTCF, CTNNA1, CTNNA3, DCAF4L2, DNAH11, DNAH2,DNAH5, DTWD2, EGFR, EP300, ERBB2, ERBB3, ERBB4, FAM20C, FANCA, FANCD2,FBXW7, FLT3, FOXO1, FOXO3, FOXP1, FRMD3, GATA3, GH1, GLDC, GPR124,GPR32, GPS2, HDAC9, HERC2, HIST1H2BC, HRAS, JAK1, KDM3A, KDM6A, KLRG1,KMT2C, KRAS, L1CAM, LAMA2, LAMB3, LARGE, LDLRAP1, LIFR, LIPI, MAGEA8,MAP2K4, MAP3K1, MAP3K10, MAP3K13, MBL2, MEN1, LL2, MLLT4, MTAP, MUC16,MYH9, MYO1A, MYO3A, NCOA3, NCOR1, NCOR2, NDFIP1, NEK1, NF1, NF2, NOTCH1,NPNT, NR2F1, NR3C1, NRAS, NRG3, NT5E, OR6A2, PALLD, PBRM1, PDE4DIP,PIK3CA, PIK3R1, PPP2CB, PPP2R2A, PRKACG, PRKCE, PRKCQ, PRKCZ, PRKG1,PRPS2, PRR16, PTEN, PTPN22, PTPRD, PTPRM, RASGEF1B, RB1, ROS1, RPGR,RUNX1, RYR2, SBNO1, SETD1A, SETD2, SETDB1, SF3B1, SGCD, SHANK2, SIAH1,SIK1, SIK2, SMAD2, SMAD4, SMARCB1, SMARCC1, SMARCC2, SMARCD1, SPACA1,STAB2, STK11, STMN2, SYNE1, TAF1, TAF4B, TBL1XR1, TBX3, TG, THADA,THSD7A, TP53, TTYH1, UBR5, USH2A, USP28, USP9×, UTRN, and ZFP36L1. Manyof these genes may be clinically actionable, such that an observedanomaly in MAF (e.g., significantly higher or lower than in normalcontrol subjects) may be indicative of a clinical state relevant tobreast cancer, such as diagnosis, prognosis, risk stratification,treatment selection, tumor resistance to treatment, tumor burden, etc.Such a breast cancer targeted panel may comprise a relatively smallnumber of these breast cancer associated genes.

In some cases, the one or more regions in a panel for colorectal cancercan comprise one or more loci from a plurality of genes, including oneof, two of, three of, four of, five of, or six of TP53, BRAF, KRAS, APC,TGFBR, and PIK3CA. Many of these genes may be clinically actionable,such that an observed anomaly in MAF (e.g., significantly higher orlower than in normal control subjects) may be indicative of a clinicalstate relevant to colorectal cancer, such as diagnosis, prognosis, riskstratification, treatment selection, tumor resistance to treatment,tumor burden, etc. Such a colorectal cancer targeted panel may comprisea relatively small number of these colorectal cancer associated genes.

In some embodiments, the one or more regions in the panel comprise oneor more loci from one or a plurality of genes for detecting residualcancer after surgery. This detection can be earlier than is possible forexisting methods of cancer detection. In some embodiments, the one ormore regions in the panel comprise one or more loci from one or aplurality of genes for detecting cancer in a high-risk patientpopulation. For example, smokers have much higher rates of lung cancerthan the general population. Moreover, smokers can develop other lungconditions that make cancer detection more difficult, such as thedevelopment of irregular nodules in the lungs. In some embodiments, themethods described herein detect cancer in high risk patients earlierthan is possible for existing methods of cancer detection.

A region may be selected for inclusion in a sequencing panel based on anumber of subjects with a cancer that have a tumor marker in that geneor region. A region may be selected for inclusion in a sequencing panelbased on prevalence of subjects with a cancer and a tumor marker presentin that gene. Presence of a tumor marker in a region may be indicativeof a subject having cancer.

In some instances, the panel may be selected using information from oneor more databases. The information regarding a cancer may be derivedfrom cancer tumor biopsies or cfDNA assays. A database may compriseinformation describing a population of sequenced tumor samples. Adatabase may comprise information about mRNA expression in tumorsamples. A databased may comprise information about regulatory elementsin tumor samples. The information relating to the sequenced tumorsamples may include the frequency various genetic variants and describethe genes or regions in which the genetic variants occur. The geneticvariants may be tumor markers. A non-limiting example of such a databaseis COSMIC. COSMIC is a catalogue of somatic mutations found in variouscancers. For a particular cancer, COSMIC ranks genes based on frequencyof mutation. A gene may be selected for inclusion in a panel by having ahigh frequency of mutation within a given gene. For instance, COSMICindicates that 33% of a population of sequenced breast cancer sampleshave a mutation in TP53 and 22% of a population of sampled breastcancers have a mutation in KRAS. Other ranked genes, including APC, havemutations found only in about 4% of a population of sequenced breastcancer samples. TP53 and KRAS may be included in a sequencing panelbased on having relatively high frequency among sampled breast cancers(compared to APC, for example, which occurs at a frequency of about 4%).COSMIC is provided as a non-limiting example, however, any database orset of information may be used that associates a cancer with tumormarker located in a gene or genetic region. In another example, asprovided by COSMIC, of 1156 biliary tract cancer samples, 380 samples(33%) carried mutations in TP53. Several other genes, such as APC, havemutations in 4-8% of all samples. Thus, TP53 may be selected forinclusion in the panel based on a relatively high frequency in apopulation of biliary tract cancer samples.

A gene or region may be selected for a panel where the frequency of atumor marker is significantly greater in sampled tumor tissue orcirculating tumor DNA than found in a given background population. Acombination of regions may be selected for inclusion of a panel suchthat at least a majority of subjects having a cancer will have a tumormarker present in at least one of the regions or genes in the panel. Thecombination of regions may be selected based on data indicating that,for a particular cancer or set of cancers, a majority of subjects haveone or more tumor markers in one or more of the selected regions. Forexample, to detect cancer 1, a panel comprising regions A, B, C, and/orD may be selected based on data indicating that 90% of subjects withcancer 1 have a tumor marker in regions A, B, C, and/or D of the panel.Alternately, tumor markers may be shown to occur independently in two ormore regions in subjects having a cancer such that, combined, a tumormarker in the two or more regions is present in a majority of apopulation of subjects having a cancer. For example, to detect cancer 2,a panel comprising regions X, Y, and Z may be selected based on dataindicating that 90% of subjects have a tumor marker in one or moreregions, and in 30% of such subjects a tumor marker is detected only inregion X, while tumor markers are detected only in regions Y and/or Zfor the remainder of the subjects for whom a tumor marker was detected.Tumor markers present in one or more regions previously shown to beassociated with one or more cancers may be indicative of or predictiveof a subject having cancer if a tumor marker is detected in one or moreof those regions 50% or more of the time. Computational approaches suchas models employing conditional probabilities of detecting cancer givena known cancer frequency for a set of tumor markers within one or moreregions may be used to predict which regions, alone or in combination,may be predictive of cancer. Other approaches for panel selectioninvolve the use of databases describing information from studiesemploying comprehensive genomic profiling of tumors with large panelsand/or whole genome sequencing (WGS, RNA-seq, Chip-seq, bisulfatesequencing, ATAC-seq, and others). Information gleaned from literaturemay also describe pathways commonly affected and mutated in certaincancers. Panel selection may be further informed by the use ofontologies describing genetic information.

Genes included in the panel for sequencing can include the fullytranscribed region, the promoter region, enhancer regions, regulatoryelements, and/or downstream sequence. To further increase the likelihoodof detecting tumor indicating mutations only exons may be included inthe panel. The panel can comprise all exons of a selected gene, or onlyone or more of the exons of a selected gene. The panel may comprise ofexons from each of a plurality of different genes. The panel maycomprise at least one exon from each of the plurality of differentgenes.

In some aspects, a panel of exons from each of a plurality of differentgenes is selected such that a determined proportion of subjects having acancer exhibit a genetic variant in at least one exon in the panel ofexons.

At least one full exon from each different gene in a panel of genes maybe sequenced. The sequenced panel may comprise exons from a plurality ofgenes. The panel may comprise exons from 2 to 100 different genes, from2 to 70 genes, from 2 to 50 genes, from 2 to 30 genes, from 2 to 15genes, or from 2 to 10 genes.

A selected panel may comprise a varying number of exons. The panel maycomprise from 2 to 3000 exons. The panel may comprise from 2 to 1000exons. The panel may comprise from 2 to 500 exons. The panel maycomprise from 2 to 100 exons. The panel may comprise from 2 to 50 exons.The panel may comprise no more than 300 exons. The panel may comprise nomore than 200 exons. The panel may comprise no more than 100 exons. Thepanel may comprise no more than 50 exons. The panel may comprise no morethan 40 exons. The panel may comprise no more than 30 exons. The panelmay comprise no more than 25 exons. The panel may comprise no more than20 exons. The panel may comprise no more than 15 exons. The panel maycomprise no more than 10 exons. The panel may comprise no more than 9exons. The panel may comprise no more than 8 exons. The panel maycomprise no more than 7 exons.

The panel may comprise one or more exons from a plurality of differentgenes. The panel may comprise one or more exons from each of aproportion of the plurality of different genes. The panel may compriseat least two exons from each of at least 25%, 50%, 75% or 90% of thedifferent genes. The panel may comprise at least three exons from eachof at least 25%, 50%, 75% or 90% of the different genes. The panel maycomprise at least four exons from each of at least 25%, 50%, 75% or 90%of the different genes.

The sizes of the sequencing panel may vary. A sequencing panel may bemade larger or smaller (in terms of nucleotide size) depending onseveral factors including, for example, the total amount of nucleotidessequenced or a number of unique molecules sequenced for a particularregion in the panel. The sequencing panel can be sized 5 kb to 50 kb.The sequencing panel can be 10 kb to 30 kb in size. The sequencing panelcan be 12 kb to 20 kb in size. The sequencing panel can be 12 kb to 60kb in size. The sequencing panel can be at least 10 kb, 12 kb, 15 kb, 20kb, 25 kb, 30 kb, 35 kb, 40 kb, 45 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90kb, 100 kb, 110 kb, 120 kb, 130 kb, 140 kb, or 150 kb in size. Thesequencing panel may be less than 100 kb, 90 kb, 80 kb, 70 kb, 60 kb, or50 kb in size.

The panel selected for sequencing can comprise at least 1, 5, 10, 15,20, 25, 30, 40, 50, 60, 80, or 100 regions. In some cases, the regionsin the panel are selected that the size of the regions are relativelysmall. In some cases, the regions in the panel have a size of about 10kb or less, about 8 kb or less, about 6 kb or less, about 5 kb or less,about 4 kb or less, about 3 kb or less, about 2.5 kb or less, about 2 kbor less, about 1.5 kb or less, or about 1 kb or less or less. In somecases, the regions in the panel have a size from about 0.5 kb to about10 kb, from about 0.5 kb to about 6 kb, from about 1 kb to about 11 kb,from about 1 kb to about 15 kb, from about 1 kb to about 20 kb, fromabout 0.1 kb to about 10 kb, or from about 0.2 kb to about 1 kb. Forexample, the regions in the panel can have a size from about 0.1 kb toabout 5 kb.

The panel selected herein can allow for deep sequencing that issufficient to detect low-frequency genetic variants (e.g., in cell-freenucleic acid molecules obtained from a sample). An amount of geneticvariants in a sample may be referred to in terms of the minor allelefrequency for a given genetic variant. The minor allele frequency mayrefer to the frequency at which minor alleles (e.g., not the most commonallele) occurs in a given population of nucleic acids, such as a sample.Genetic variants at a low minor allele frequency may have a relativelylow frequency of presence in a sample. In some cases, the panel allowsfor detection of genetic variants at a minor allele frequency of atleast 0.0001%, 0.001%, 0.005%, 0.01%, 0.05%, 0.1%, or 0.5%. The panelcan allow for detection of genetic variants at a minor allele frequencyof 0.001% or greater. The panel can allow for detection of geneticvariants at a minor allele frequency of 0.01% or greater. The panel canallow for detection of genetic variant present in a sample at afrequency of as low as 0.0001%, 0.001%, 0.005%, 0.01%, 0.025%, 0.05%,0.075%, 0.1%, 0.25%, 0.5%, 0.75%, or 1.0%. The panel can allow fordetection of tumor markers present in a sample at a frequency of atleast 0.0001%, 0.001%, 0.005%, 0.01%, 0.025%, 0.05%, 0.075%, 0.1%,0.25%, 0.5%, 0.75%, or 1.0%. The panel can allow for detection of tumormarkers at a frequency in a sample as low as 1.0%. The panel can allowfor detection of tumor markers at a frequency in a sample as low as0.75%. The panel can allow for detection of tumor markers at a frequencyin a sample as low as 0.5%. The panel can allow for detection of tumormarkers at a frequency in a sample as low as 0.25%. The panel can allowfor detection of tumor markers at a frequency in a sample as low as0.1%. The panel can allow for detection of tumor markers at a frequencyin a sample as low as 0.075%. The panel can allow for detection of tumormarkers at a frequency in a sample as low as 0.05%. The panel can allowfor detection of tumor markers at a frequency in a sample as low as0.025%. The panel can allow for detection of tumor markers at afrequency in a sample as low as 0.01%. The panel can allow for detectionof tumor markers at a frequency in a sample as low as 0.005%. The panelcan allow for detection of tumor markers at a frequency in a sample aslow as 0.001%. The panel can allow for detection of tumor markers at afrequency in a sample as low as 0.0001%. The panel can allow fordetection of tumor markers in sequenced cfDNA at a frequency in a sampleas low as 1.0% to 0.0001%. The panel can allow for detection of tumormarkers in sequenced cfDNA at a frequency in a sample as low as 0.01% to0.0001%.

A genetic variant can be exhibited in a percentage of a population ofsubjects who have a disease (e.g., cancer). In some cases, at least 1%,2%, 3%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% ofa population having the cancer exhibit one or more genetic variants inat least one of the regions in the panel. For example, at least 80% of apopulation having the cancer may exhibit one or more genetic variants inat least one of the regions in the panel.

The panel can comprise one or more regions from each of one or moregenes. In some cases, the panel can comprise one or more regions fromeach of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50,or 80 genes. In some cases, the panel can comprise one or more regionsfrom each of at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40,50, or 80 genes. In some cases, the panel can comprise one or moreregions from each of from about 1 to about 80, from 1 to about 50, fromabout 3 to about 40, from 5 to about 30, from 10 to about 20 differentgenes.

The regions in the panel can be selected so that one or moreepigenetically modified regions are detected. The one or moreepigenetically modified regions can be acetylated, methylated,ubiquitylated, phosphorylated, sumoylated, ribosylated, and/orcitrullinated. For example, the regions in the panel can be selected sothat one or more methylated regions are detected.

The regions in the panel can be selected so that they comprise sequencesdifferentially transcribed across one or more tissues. In some cases,the regions can comprise sequences transcribed in certain tissues at ahigher level compared to other tissues. For example, the regions cancomprise sequences transcribed in certain tissues but not in othertissues.

The regions in the panel can comprise coding and/or non-codingsequences. For example, the regions in the panel can comprise one ormore sequences in exons, introns, promoters, 3′ untranslated regions, 5′untranslated regions, regulatory elements, transcription start sites,and/or splice sites. In some cases, the regions in the panel cancomprise other non-coding sequences, including pseudogenes, repeatsequences, transposons, viral elements, and telomeres. In some cases,the regions in the panel can comprise sequences in non-coding RNA, e.g.,ribosomal RNA, transfer RNA, Piwi-interacting RNA, and microRNA.

The regions in the panel can be selected to detect (diagnose) a cancerwith a desired level of sensitivity (e.g., through the detection of oneor more genetic variants). For example, the regions in the panel can beselected to detect the cancer (e.g., through the detection of one ormore genetic variants) with a sensitivity of at least 50%, 55%, 60%,65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.The regions in the panel can be selected to detect the cancer with asensitivity of 100%.

The regions in the panel can be selected to detect (diagnose) a cancerwith a desired level of specificity (e.g., through the detection of oneor more genetic variants). For example, the regions in the panel can beselected to detect cancer (e.g., through the detection of one or moregenetic variants) with a specificity of at least 50%, 55%, 60%, 65%,70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. Theregions in the panel can be selected to detect the one or more geneticvariant with a specificity of 100%.

The regions in the panel can be selected to detect (diagnose) a cancerwith a desired positive predictive value. Positive predictive value canbe increased by increasing sensitivity (e.g., chance of an actualpositive being detected) and/or specificity (e.g., chance of notmistaking an actual negative for a positive). As a non-limiting example,regions in the panel can be selected to detect the one or more geneticvariant with a positive predictive value of at least 50%, 55%, 60%, 65%,70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. Theregions in the panel can be selected to detect the one or more geneticvariant with a positive predictive value of 100%.

The regions in the panel can be selected to detect (diagnose) a cancerwith a desired accuracy. As used herein, the term “accuracy” may referto the ability of a test to discriminate between a disease condition(e.g., cancer) and health. Accuracy may be can be quantified usingmeasures such as sensitivity and specificity, predictive values,likelihood ratios, the area under the ROC curve, Youden's index and/ordiagnostic odds ratio.

Accuracy may presented as a percentage, which refers to a ratio betweenthe number of tests giving a correct result and the total number oftests performed. The regions in the panel can be selected to detectcancer with an accuracy of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%,85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. The regions in thepanel can be selected to detect cancer with an accuracy of 100%.

A panel may be selected such that when one or more regions or genes inthe panel are removed, specificity is appreciably decreased. Removal ofone region from the panel may result in a decrease in specificity of atleast 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, or more.

A panel may be selected such that the addition of one or more regions orgenes to the panel does not appreciably increase the specificity of thepanel, e.g., does not increase the specificity by more than 1%, 2%, 5%,10%, 15%, or 20%.

A panel may be of a size such that when one or more regions or genes inthe panel are removed, this appreciably decreases sensitivity, e.g.,sensitivity is decreased by at least 5%, 10%, 15%, 20%, 25%, 30%, 35%,40%, 45%, 50%, or more.

A panel may be selected such that the addition of one or more regions orgenes to the panel does not appreciably increase the sensitivity of thepanel, e.g., does not increase the sensitivity by more than 1%, 2%, 5%,10%, 15%, or 20%.

A panel may be of a size such that when one or more regions or genes inthe panel are removed, accuracy is appreciably decreased, e.g., accuracyis decreased by at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%,50%, or more.

A panel may be selected such that the addition of one or more regions orgenes to the panel does not appreciably increase the accuracy of thepanel, e.g., does not increase the accuracy by more than 1%, 2%, 5%,10%, 15%, or 20%.

A panel may be of a size such that when one or more regions or genes thepanel are removed, positive predictive value is appreciably decreased,e.g., positive predictive value is decreased by at least 5%, 10%, 15%,20%, 25%, 30%, 35%, 40%, 45%, 50%, or more.

A panel may be selected such that the addition of one or more regions orgenes to the panel does not appreciably increase the positive predictivevalue of the panel, e.g., does not increase the positive predictivevalue by more than 1%, 2%, 5%, 10%, 15%, or 20%

A panel may be selected to be highly sensitive and detect low frequencygenetic variants. For instance, a panel may be selected such that agenetic variant or tumor marker present in a sample at a frequency aslow as 0.01%, 0.05%, or 0.001% may be detected at a sensitivity of atleast 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%,99%, 99.5%, or 99.9%. Regions in a panel may be selected to detect atumor marker present at a frequency of 1% or less in a sample with asensitivity of 70% or greater. A panel may be selected to detect a tumormarker at a frequency in a sample as low as 0.1% with a sensitivity ofat least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%,98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a tumormarker at a frequency in a sample as low as 0.01% with a sensitivity ofat least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%,98%, 99%, 99.5%, or 99.9%. A panel may be selected to detect a tumormarker at a frequency in a sample as low as 0.001% with a sensitivity ofat least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%,98%, 99%, 99.5%, or 99.9%.

A panel may be selected to be highly specific and detect low frequencygenetic variants. For instance, a panel may be selected such that agenetic variant or tumor marker present in a sample at a frequency aslow as 0.01%, 0.05%, or 0.001% may be detected at a specificity of atleast 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%,99%, 99.5%, or 99.9%. Regions in a panel may be selected to detect atumor marker present at a frequency of 1% or less in a sample with aspecificity of 70% or greater. A panel may be selected to detect a tumormarker at a frequency in a sample as low as 0.1% with a specificity ofat least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or99.9%. A panel may be selected to detect a tumor marker at a frequencyin a sample as low as 0.01% with a specificity of at least 70%, 75%,80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. A panel may beselected to detect a tumor marker at a frequency in a sample as low as0.001% with a specificity of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%,97%, 98%, 99%, 99.5%, or 99.9%.

A panel may be selected to be highly accurate and detect low frequencygenetic variants. A panel may be selected such that a genetic variant ortumor marker present in a sample at a frequency as low as 0.01%, 0.05%,or 0.001% may be detected at an accuracy of at least 70%, 75%, 80%, 85%,90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. Regions in a panel may beselected to detect a tumor marker present at a frequency of 1% or lessin a sample with an accuracy of 70% or greater. A panel may be selectedto detect a tumor marker at a frequency in a sample as low as 0.1% withan accuracy of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%,99%, 99.5%, or 99.9%. A panel may be selected to detect a tumor markerat a frequency in a sample as low as 0.01% with an accuracy of at least70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%. Apanel may be selected to detect a tumor marker at a frequency in asample as low as 0.001% with an accuracy of at least 70%, 75%, 80%, 85%,90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.

A panel may be selected to be highly predictive and detect low frequencygenetic variants. A panel may be selected such that a genetic variant ortumor marker present in a sample at a frequency as low as 0.01%, 0.05%,or 0.001% may have a positive predictive value of at least 70%, 75%,80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.

The concentration of probes or baits used in the panel may be increased(2 to 6 ng/μL) to capture more nucleic acid molecule within a sample.The concentration of probes or baits used in the panel may be at least 2ng/μL, 3 ng/μL, 4 ng/5 ng/μL, 6 ng/μL, or greater. The concentration ofprobes may be about 2 ng/μL to about 3 ng/μL, about 2 ng/μL to about 4ng/μL, about 2 ng/μL to about 5 ng/μL, about 2 ng/μL to about 6 ng/μL.The concentration of probes or baits used in the panel may be 2 ng/μL ormore to 6 ng/μL or less. In some instances this may allow for moremolecules within a biological to be analyzed thereby enabling lowerfrequency alleles to be detected.

Sequencing Depth

DNA enriched from a sample of cfDNA molecules may be sequenced at avariety of read depths to detect low frequency genetic variants in asample. For a given position, read depth may refer to a number of allreads from all molecules from a sample that map to a position, includingoriginal molecules and molecules generated by amplifying originalmolecules. Thus, for example, a read depth of 50,000 reads can refer tothe number of reads from 5,000 molecules, with 10 reads per molecule.Original molecules mapping to a position may be unique and non-redundant(e.g., non-amplified, sample cfDNA).

To assess read depth of sample molecules at a given position, samplemolecules may be tracked. Molecular tracking techniques may comprisevarious techniques for labeling DNA molecules, such as barcode tagging,to uniquely identify DNA molecules in a sample. For example, one or moreunique barcode sequences may be attached to one or more ends of a samplecfDNA molecule. In determining read depth at a given position, thenumber of distinct barcode tagged cfDNA molecules which map to thatposition can be indicative of the read depth for that position. Inanother example, both ends of sample cfDNA molecules may be tagged withone of eight barcode sequences. The read depth at a given position maybe determined by quantifying the number of original cfDNA molecules at agiven position, for instance, by collapsing reads that are redundantfrom amplification and identifying unique molecules based on the barcodetags and endogenous sequence information.

The DNA may be sequenced to a read depth of at least 3,000 reads perbase, at least 4,000 reads per base, at least 5,000 reads per base, atleast 6,000 reads per base, at least 7,000 reads per base, at least8,000 reads per base, at least 9,000 reads per base, at least 10,000reads per base, at least 15,000 reads per base, at least 20,000 readsper base, at least 25,000 reads per base, at least 30,000 reads perbase, at least 40,000 reads per base, at least 50,000 reads per base, atleast 60,000 reads per base, at least 70,000 reads per base, at least80,000 reads per base, at least 90,000 reads per base, at least 100,000reads per base, at least 110,000 reads per base, at least 120,000 readsper base, at least 130,000 reads per base, at least 140,000 reads perbase, at least 150,000 reads per base, at least 160,000 reads per base,at least 170,000 reads per base, at least 180,000 reads per base, atleast 190,000 reads per base, at least 200,000 reads per base, at least250,000 reads per base, at least 500,000 reads per base, at least1,000,000 reads per base, or at least 2,000,000 reads per base. The DNAmay be sequenced to a read depth of about 3,000 reads per base, about4,000 reads per base, about 5,000 reads per base, about 6,000 reads perbase, about 7,000 reads per base, about 8,000 reads per base, about9,000 reads per base, about 10,000 reads per base, about 15,000 readsper base, about 20,000 reads per base, about 25,000 reads per base,about 30,000 reads per base, about 40,000 reads per base, about 50,000reads per base, about 60,000 reads per base, about 70,000 reads perbase, about 80,000 reads per base, about 90,000 reads per base, about100,000 reads per base, about 110,000 reads per base, about 120,000reads per base, about 130,000 reads per base, about 140,000 reads perbase, about 150,000 reads per base, about 160,000 reads per base, about170,000 reads per base, about 180,000 reads per base, about 190,000reads per base, about 200,000 reads per base, about 250,000 reads perbase, about 500,000 reads per base, about 1,000,000 reads per base, orabout 2,000,000 reads per base. The DNA can be sequenced to a read depthfrom about 10,000 to about 30,000 reads per base, 10,000 to about 50,000reads per base, 10,000 to about 5,000,000 reads per base, 50,000 toabout 3,000,000 reads per base, 100,000 to about 2,000,000 reads perbase, or about 500,000 to about 1,000,000 reads per base. In someembodiments, DNA can be sequenced to any of the above read depths on apanel size selected from: less than 70,000 bases, less than 65,000bases, less than 60,000 bases, less than 55,000 bases, less than 50,000bases, less than 45,000 bases, less than 40,000 bases, less than 35,000bases, less than 30,000 bases, less than 25,000 bases, less than 20,000bases, less than 15,000 bases, less than 10,000 bases, less than 5,000bases, and less than 1,000 bases. For example, the total number of readsfor a panel can be as low as 600,000 (3,000 reads per base for 1,000bases) and as high as 1.4×10¹¹ (2,000,000 reads per base for 70,000bases). In some embodiments, DNA can be sequenced to any of the aboveread depths on a panel size selected from: 5,000 bases to 70,000 bases,5,000 bases to 60,000 bases, 10,000 bases to 70,000 bases, or 10,000bases to 70,000 bases.

Read coverage can include reads from one or both strands of a nucleicacid molecule. For example, read coverage may include reads from bothstrands of at least 5,000, at least 10,000, at least 15,000, at least20,000, at least 25,000, at least 30,000, at least 35,000, at least40,000, at least 45,000, or at least 50,000 DNA molecules from thesample mapping to each nucleotide in the of the panel.

A panel may be selected to optimize for a desired read depth given afixed amount of base reads.

Tagging

In some embodiments of the present disclosure, a nucleic acid library isprepared prior to sequencing. For example, individual polynucleotidefragments in a genomic nucleic acid sample (e.g., genomic DNA sample)can be uniquely identified by tagging with non-unique identifiers, e.g.,non-uniquely tagging the individual polynucleotide fragments. In someembodiments, nucleic acid molecules are non-uniquely tagged with respectto one another.

Polynucleotides disclosed herein can be tagged. For example,double-stranded polynucleotides can be tagged with duplex tags, tagsthat differently label the complementary strands (i.e., the “Watson” and“Crick” strands) of a double-stranded molecule. In some cases the duplextags are polynucleotides having complementary and non-complementaryportions.

Tags can be any types of molecules attached to a polynucleotide,including, but not limited to, nucleic acids, chemical compounds,florescent probes, or radioactive probes. Tags can also beoligonucleotides (e.g., DNA or RNA). Tags can comprise known sequences,unknown sequences, or both. A tag can comprise random sequences,pre-determined sequences, or both. A tag can be double-stranded orsingle-stranded. A double-stranded tag can be a duplex tag. Adouble-stranded tag can comprise two complementary strands.Alternatively, a double-stranded tag can comprise a hybridized portionand a non-hybridized portion. The double-stranded tag can be Y-shaped,e.g., the hybridized portion is at one end of the tag and thenon-hybridized portion is at the opposite end of the tag. One suchexample is the “Y adapters” used in Illumina sequencing. Other examplesinclude hairpin shaped adapters or bubble shaped adapters. Bubble shapedadapters have non-complementary sequences flanked on both sides bycomplementary sequences. In some embodiments, a Y-shaped adaptorcomprises a barcode 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, or 32nucleotides in length. In some combinations. This can be combined withblunt end repair and ligation.

The number of different tags may be greater than an estimated orpredetermined number of molecules in the sample. For example, for uniquetagging, at least two times as many different tags may be used as theestimated or predetermined number of molecules in the sample.

The number of different identifying tags used to tag molecules in acollection can range, for example, between any of 2, 3, 4, 5, 6, 7, 8,9, 10, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, or49 at the low end of the range, and any of 50, 100, 500, 1000, 5000 and10,000 at the high end of the range. The number of identifying tags usedto tag molecules in a collection can be at least 2, 3, 4, 5, 6, 7, 8, 9,10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 or more. So, for example, acollection of from 100 billion to 1 trillion molecules can be taggedwith from 4 to 100 different identifying tags. A collection of from 100billion to 1 trillion molecules may be tagged with from 8 to 10,000different identifying tags. A collection of from 100 billion to 1trillion molecules may be tagged with from 16 to 10,000 differentidentifying tags. A collection of from 100 billion to 1 trillionmolecules may be tagged with from 16 to 5,000 different identifyingtags. A collection of from 100 billion to 1 trillion molecules may betagged with from 16 to 1,000 different identifying tags.

A collection of molecules can be considered to be “non-uniquely tagged”if there are more molecules in the collection than tags. A collection ofmolecules can be considered to be non-uniquely tagged if each of atleast 1%, at least 5%, at least 10%, at least 15%, at least 20%, atleast 25%, at least 30%, at least 35%, at least 40%, at least 45%, or atleast or about 50% of the molecules in the collection bears anidentifying tag that is shared by at least one other molecule in thecollection (“non-unique tag” or “non-unique identifier”). An identifiercan comprise a single barcode or two barcodes. A population of nucleicacid molecules can be non-uniquely tagged by tagging the nucleic acidmolecules with fewer tags than the total number of nucleic acidmolecules in the population. For a non-uniquely tagged population, nomore than 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% of themolecules may be uniquely tagged. In some embodiments, nucleic acidmolecules are identified by a combination of non-unique tags and thestart and stop positions or sequences from sequence reads. In someembodiments, the number of nucleic acid molecules being sequenced isless than or equal to the number of combinations of identifiers andstart and stop positions or sequences.

In some instances, the tags herein comprise molecular barcodes. Suchmolecular barcodes can be used to differentiate polynucleotides in asample. Molecular barcodes can be different from one another. Forexample, molecular barcodes can have a difference between them that canbe characterized by a predetermined edit distance or a Hamming distance.In some instances, the molecular barcodes herein have a minimum editdistance of 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10. To further improveefficiency of conversion (e.g., tagging) of untagged molecular to taggedmolecules, one utilizes short tags. For example, a library adapter tagcan be up to 65, 60, 55, 50, 45, 40, or 35 nucleotide bases in length. Acollection of such short library barcodes can include a number ofdifferent molecular barcodes, e.g., at least 2, 4, 6, 8, 10, 12, 14, 16,18 or 20 different barcodes with a minimum edit distance of 1, 2, 3 ormore.

Thus, a collection of molecules can include one or more tags. In someinstances, some molecules in a collection can include an identifying tag(“identifier”) such as a molecular barcode that is not shared by anyother molecule in the collection. For example, in some instances of acollection of molecules, 100% or at least 50%, 60%, 70%, 80%, 90%, 95%,97%, 98%, or 99% of the molecules in the collection can include anidentifier or molecular barcode that is not shared by any other moleculein the collection. As used herein, a collection of molecules isconsidered to be “uniquely tagged” if each of at least 95% of themolecules in the collection bears an identifier that is not shared byany other molecule in the collection (“unique tag” or “uniqueidentifier”). In some embodiments, nucleic acid molecules are uniquelytagged with respect to one another. A collection of molecules isconsidered to be “non-uniquely tagged” if each of at least 1%, 5%, 10%,15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% of the molecules in thecollection bears an identifying tag or molecular barcode that is sharedby at least one other molecule in the collection (“non-unique tag” or“non-unique identifier”). In some embodiments, nucleic acid moleculesare non-uniquely tagged with respect to one another. Accordingly, in anon-uniquely tagged population no more than 1% of the molecules areuniquely tagged. For example, in a non-uniquely tagged population, nomore than 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% of themolecules can be uniquely tagged.

A number of different tags can be used based on the estimated number ofmolecules in a sample. In some tagging methods, the number of differenttags can be at least the same as the estimated number of molecules inthe sample. In other tagging methods, the number of different tags canbe at least two, three, four, five, six, seven, eight, nine, ten, onehundred or one thousand times as many as the estimated number ofmolecules in the sample. In unique tagging, at least two times (or more)as many different tags can be used as the estimated number of moleculesin the sample.

The polynucleotides fragments (prior to tagging) can comprise sequencesof any length. For example, polynucleotide fragments (prior to tagging)can comprise at least 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105,110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175,180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245,250, 255, 260, 265, 270, 275, 280, 285, 290, 295, 300, 400, 500, 600,700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,1900, 2000 or more nucleotides in length. The polynucleotide fragmentcan be about the average length of cell-free DNA. For example, thepolynucleotide fragments can comprise about 160 bases in length. Thepolynucleotide fragment can also be fragmented from a larger fragmentinto smaller fragments about 160 bases in length.

Improvements in sequencing can be achieved as long as at least some ofthe duplicate or cognate polynucleotides bear unique identifiers withrespect to each other, that is, bear different tags. However, in certainembodiments, the number of tags used is selected so that there is atleast a 95% chance that all duplicate molecules starting at any oneposition bear unique identifiers. For example, in a sample comprisingabout 10,000 haploid human genome equivalents of fragmented genomic DNA,e.g., cfDNA, z is expected to be between 2 and 8. Such a population canbe tagged with between about 10 and 100 different identifiers, forexample, about 2 identifiers, about 4 identifiers, about 9 identifiers,about 16 identifiers, about 25 identifiers, about 36 differentidentifiers, about 49 different identifiers, about 64 differentidentifiers, about 81 different identifiers, or about 100 differentidentifiers.

Nucleic acid barcodes having identifiable sequences, including molecularbarcodes, can be used for tagging. For example, a plurality of DNAbarcodes can comprise various numbers of sequences of nucleotides. Aplurality of DNA barcodes having 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 ormore identifiable sequences of nucleotides can be used. When attached toonly one end of a polynucleotide, the plurality of DNA barcodes canproduce 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or more differentidentifiers. Alternatively, when attached to both ends of apolynucleotide, the plurality DNA barcodes can produce 4, 9, 16, 25, 36,49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400 ormore different identifiers (which is the 2 of when the DNA barcode isattached to only 1 end of a polynucleotide). In one example, a pluralityof DNA barcodes having 6, 7, 8, 9 or 10 identifiable sequences ofnucleotides can be used. When attached to both ends of a polynucleotide,they produce 36, 49, 64, 81 or 100 possible different identifiers,respectively. In a particular example, the plurality of DNA barcodes cancomprise 8 identifiable sequences of nucleotides. When attached to onlyone end of a polynucleotide, the plurality of DNA barcodes can produce 8different identifiers. Alternatively, when attached to both ends of apolynucleotide, the plurality of DNA barcodes can produce 64 differentidentifiers. Samples tagged in such a way can be those with a range ofabout 10 ng to any of about 200 ng, about 1 μg, about 10 μg offragmented polynucleotides, e.g., genomic DNA, e.g., cfDNA.

A polynucleotide can be uniquely identified in various ways. Apolynucleotide can be uniquely identified by a unique barcode. Forexample, any two polynucleotides in a sample are attached two differentbarcodes. A barcode may be a DNA barcode or an RNA barcode. For example,a barcode may be a DNA barcode.

Alternatively, a polynucleotide can be uniquely identified by thecombination of a barcode and one or more endogenous sequences of thepolynucleotide. The barcode may be a non-unique tag or a unique tag. Insome cases, the barcode is a non-unique tag. For example, any twopolynucleotides in a sample can be attached to barcodes comprising thesame barcode, but the two polynucleotides can still be identified bydifferent endogenous sequences. The two polynucleotides may beidentified by information in the different endogenous sequences. Suchinformation includes the sequence of the endogenous sequences or aportion thereof, the length of the endogenous sequences, the location ofthe endogenous sequences, one or more epigenetic modification of theendogenous sequences, or any other feature of the endogenous sequences.In some embodiments, polynucleotides can be identified by an identifier(comprising one barcode or comprising two barcodes) in combination withstart and stop sequences from the sequence read.

A combination of non-unique tags and endogenous sequence information maybe used to unambiguously detect nucleic acid molecules. For instance,non-uniquely tagged nucleic acid molecules from a sample (“parentpolynucleotides”) may be amplified to generate progeny polynucleotides.The parent and progeny polynucleotides may then be sequenced to producesequence reads. To reduce error, sequence reads may be collapsed togenerate a set of consensus sequences. To generate consensus sequences,sequence reads may be collapsed based on sequence information in thenon-unique tag and endogenous sequence information, including sequenceinformation at a beginning region of a sequence read, sequenceinformation at an end region of a sequence read, and a length of asequence read. In some embodiments, a consensus sequence is generated bycircular sequencing, in which the same nucleic acid strand is sequencedmultiple times in a rolling circle to obtain the consensus sequence. Aconsensus sequence can be determined on a molecule-by-molecule basis(wherein a consensus sequence is determined over a stretch of bases) ora base-by-base basis (wherein a consensus nucleotide is determined for abase at a given position). In some embodiments, a probabilistic model isconstructed to model amplification and sequencing error profiles andused to estimate probabilities of true nucleotide in each position ofthe molecule. In some embodiments, the probabilistic model parameterestimates are updated based on the error profiles observed in theindividual sample or batch of samples being process together or areference set of samples. In some embodiments, a consensus sequence isdetermined using barcodes that tag individual cfNA (e.g., cfDNA)molecules from a subject. In some embodiments, frequency of a nucleotidein a sample is determined by comparing it to frequency in a cohort ofhealthy individuals, a cohort of cancer patients, or germline DNA fromthe subject. In some embodiments, a cohort of cancer patients comprisesa plurality of cancer patients with cancer that is early stage or latestage. In some embodiments, the cancer is metastatic or not metastatic.Types of cancer that an individual in a cohort of cancer patients mayhave been diagnosed with include, but are not limited to: carcinomas,sarcomas, lymphomas, leukemia's, germ cell tumors and blastomas. Typesof cancer that a subject may have been diagnosed with include, but arenot limited to: Acute lymphoblastic leukemia (ALL), Acute myeloidleukemia, Adrenocortical carcinoma, adult acute Myeloid leukemia, adultcarcinoma of unknown primary site, adult malignant Mesothelioma,AIDS-related cancers, AIDS-related lymphoma, Anal cancer, Appendixcancer, Astrocytoma, childhood cerebellar or cerebral, Basal-cellcarcinoma, Bile duct cancer, Bladder cancer, Bone tumor,osteosarcoma/malignant fibrous histiocytoma, Brain cancer, Brainstemglioma, Breast cancer, Bronchial adenomas/carcinoids, Burkitt Lymphoma,Carcinoid tumor, Carcinoma of unknown primary, Central nervous systemlymphoma, cerebellar astrocytoma, cerebral astrocytoma/malignant glioma,Cervical cancer, childhood acute Myeloid leukemia, childhood cancer ofunknown primary site, Childhood cancers, childhood cerebral astrocytoma,childhood Mesothelioma, Chondrosarcoma, Chronic lymphocytic leukemia,Chronic myelogenous leukemia, Chronic myeloproliferative disorders,Colon cancer, Cutaneous T-cell lymphoma, Desmoplastic small round celltumor, Endometrial cancer, endometrial Uterine cancer, Ependymoma,Epitheliod Hemangioendothelioma (EHE), Esophageal cancer, Ewing familyof tumors Sarcoma, Ewing's sarcoma in the Ewing family of tumors,Extracranial germ cell tumor, Extragonadal germ cell tumor, Extrahepaticbile duct cancer, Eye cancer, intraocular melanoma, Gallbladder cancer,Gastric (stomach) cancer, Gastric carcinoid, Gastrointestinal carcinoidtumor, Gastrointestinal stromal tumor (GIST), Gestational trophoblastictumor, Glioma of the brain stem, Glioma, Hairy cell leukemia, Head andneck cancer, Heart cancer, Hepatocellular (liver) cancer, Hodgkinlymphoma, Hypopharyngeal cancer, Hypothalamic and visual pathway glioma,Islet cell carcinoma (endocrine pancreas), Kaposi sarcoma, Kidney cancer(renal cell cancer), Laryngeal cancer, Leukaemia, acute lymphoblastic(also called acute lymphocytic leukaemia), Leukaemia, acute myeloid(also called acute myelogenous leukemia), Leukaemia, chronic lymphocytic(also called chronic lymphocytic leukemia), Leukaemias, Leukemia,chronic myelogenous (also called chronic myeloid leukemia), Leukemia,hairy cell, Lip and oral cavity cancer, Liposarcoma, Liver cancer(primary), Lung cancer, non-small cell, Lung cancer, small cell,Lymphoma (AIDS-related), Lymphomas, Macroglobulinemia, Waldenström, Malebreast cancer, Malignant fibrous histiocytoma of bone/osteosarcoma,medulloblastoma, Melanoma, Merkel cell cancer, Metastatic squamous neckcancer with occult primary, Mouth cancer, Multiple endocrine neoplasiasyndrome, childhood, multiple Myeloma (cancer of the bone-marrow),Multiple myeloma/plasma cell neoplasm, Mycosis fungoides,Myelodysplastic syndromes, Myelodysplastic/myeloproliferative diseases,Myelogenous leukemia, chronic, Myxoma, Nasal cavity and paranasal sinuscancer, Nasopharyngeal carcinoma, Neuroblastoma, Non-Hodgkin Lymphomas,Non-small cell lung cancer, Oligodendroglioma, Oral cancer,Oropharyngeal cancer, Osteosarcoma/malignant fibrous histiocytoma ofbone, Ovarian cancer, Ovarian epithelial cancer (surfaceepithelial-stromal tumor), Ovarian germ cell tumor, Ovarian lowmalignant potential tumor, Pancreatic cancer, Pancreatic cancer, isletcell, Paranasal sinus and nasal cavity cancer, Parathyroid cancer,Penile cancer, Pharyngeal cancer, Pheochromocytoma, Pineal astrocytoma,Pineal germinoma, Pineoblastoma and supratentorial primitiveneuroectodermal tumors, Pituitary adenoma, Plasma cellneoplasia/Multiple myeloma, Pleuropulmonary blastoma, Primary centralnervous system lymphoma, Prostate cancer, Rectal cancer, Renal cellcarcinoma (kidney cancer), Renal pelvis and ureter transitional cellcancer, Retinoblastoma, Rhabdomyosarcoma, Salivary gland cancer, Sézarysyndrome, Skin cancer (melanoma), Skin cancer (non-melanoma), Skincarcinoma, Merkel cell, Small cell lung cancer, Small intestine cancer,soft tissue Sarcoma, Squamous cell carcinoma, Squamous neck cancer withoccult primary, metastatic, Stomach cancer, Supratentorial primitiveneuroectodermal tumor, T-Cell lymphoma, cutaneous, Testicular cancer,Throat cancer, Thymoma and thymic carcinoma, Thymoma, Thyroid cancer,Transitional cell cancer of the renal pelvis and ureter, Ureter andrenal pelvis, transitional cell cancer, Urethral cancer, Uterinesarcoma, Vaginal cancer, visual pathway and hypothalamic glioma, Visualpathway and hypothalamic glioma, childhood, Vulvar cancer, Waldenströmmacroglobulinemia, and Wilms tumor (kidney cancer).

The endogenous sequence can be on an end of a polynucleotide. Forexample, the endogenous sequence can be adjacent (e.g., base in between)to the attached barcode. In some instances the endogenous sequence canbe at least 2, 4, 6, 8, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 basesin length. The endogenous sequence can be a terminal sequence of thefragment/polynucleotides to be analyzed. The endogenous sequence may bethe length of the sequence. For example, a plurality of barcodescomprising 8 different barcodes can be attached to both ends of eachpolynucleotide in a sample. Each polynucleotide in the sample can beidentified by the combination of the barcodes and about 10 base pairendogenous sequence on an end of the polynucleotide. Without being boundby theory, the endogenous sequence of a polynucleotide can also be theentire polynucleotide sequence.

Also disclosed herein are compositions of tagged polynucleotides. Thetagged polynucleotide can be single-stranded. Alternatively, the taggedpolynucleotide can be double-stranded (e.g., duplex-taggedpolynucleotides). Accordingly, this disclosure also providescompositions of duplex-tagged polynucleotides. The polynucleotides cancomprise any types of nucleic acids (DNA and/or RNA). Thepolynucleotides comprise any types of DNA disclosed herein. For example,the polynucleotides can comprise DNA, e.g., fragmented DNA or cfDNA. Aset of polynucleotides in the composition that map to a mappable baseposition in a genome can be non-uniquely tagged, that is, the number ofdifferent identifiers can be at least 2 and fewer than the number ofpolynucleotides that map to the mappable base position. The number ofdifferent identifiers can also be at least 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 and fewer thanthe number of polynucleotides that map to the mappable base position.

In some instances, as a composition goes from about 1 ng to about 10 μgor higher, a larger set of different molecular barcodes can be used. Forexample, between 5 and 100 different library adaptors can be used to tagpolynucleotides in a cfDNA sample.

The molecular barcodes can be assigned to any types of polynucleotidesdisclosed in this disclosure. For example, the molecular barcodes can beassigned to cell-free polynucleotides (e.g., cfDNA). Often, anidentifier disclosed herein can be a barcode oligonucleotide that isused to tag the polynucleotide. The barcode identifier may be a nucleicacid oligonucleotide (e.g., a DNA oligonucleotide). The barcodeidentifier can be single-stranded. Alternatively, the barcode identifiercan be double-stranded. The barcode identifier can be attached topolynucleotides using any method disclosed herein. For example, thebarcode identifier can be attached to the polynucleotide by ligationusing an enzyme. The barcode identifier can also be incorporated intothe polynucleotide through PCR. In other cases, the reaction maycomprise addition of a metal isotope, either directly to the analyte orby a probe labeled with the isotope. Generally, assignment of unique ornon-unique identifiers or molecular barcodes in reactions of thisdisclosure may follow methods and systems described by, for example,U.S. patent applications 2001/0053519, 2003/0152490, 2011/0160078 andU.S. Pat. No. 6,582,908, each of which is entirely incorporated hereinby reference.

Identifiers or molecular barcodes used herein may be completelyendogenous whereby circular ligation of individual fragments may beperformed followed by random shearing or targeted amplification. In thiscase, the combination of a new start and stop point of the molecule andthe original intramolecular ligation point can form a specificidentifier.

Identifiers or molecular barcodes used herein can comprise any types ofoligonucleotides. In some cases, identifiers may be predetermined,random, or semi-random sequence oligonucleotides. Identifiers can bebarcodes. For example, a plurality of barcodes may be used such thatbarcodes are not necessarily unique to one another in the plurality.Alternatively, a plurality of barcodes may be used such that eachbarcode is unique to any other barcode in the plurality. The barcodescan comprise specific sequences (e.g., predetermined sequences) that canbe individually tracked. Further, barcodes may be attached (e.g., byligation) to individual molecules such that the combination of thebarcode and the sequence it may be ligated to creates a specificsequence that may be individually tracked. As described herein,detection of barcodes in combination with sequence data of beginning(start) and/or end (stop) portions of sequence reads can allowassignment of a unique identity to a particular molecule. The length ornumber of base pairs of an individual sequence read may also be used toassign a unique identity to such a molecule. As described herein,fragments from a single strand of nucleic acid having been assigned aunique identity, may thereby permit subsequent identification offragments from the parent strand. In this way the polynucleotides in thesample can be uniquely or substantially uniquely tagged. A duplex tagcan include a degenerate or semi-degenerate nucleotide sequence, e.g., arandom degenerate sequence. The nucleotide sequence can comprise anynumber of nucleotides. For example, the nucleotide sequence can comprise1 (if using a non-natural nucleotide), 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,48, 49, 50 or more nucleotides. In a particular example, the sequencecan comprise 7 nucleotides. In another example, the sequence cancomprise 8 nucleotides. The sequence can also comprise 9 nucleotides.The sequence can comprise 10 nucleotides.

A barcode can comprise contiguous or non-contiguous sequences. A barcodethat comprises at least 1, 2, 3, 4, 5 or more nucleotides is acontiguous sequence or non-contiguous sequence. if the 4 nucleotides areuninterrupted by any other nucleotide. For example, if a barcodecomprises the sequence TTGC, a barcode is contiguous if the barcode isTTGC. On the other hand, a barcode is non-contiguous if the barcode isTTXGC, where X is a nucleic acid base.

An identifier or molecular barcode can have an n-mer sequence which maybe 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 or more nucleotides inlength. A tag herein can comprise any range of nucleotides in length.For example, the sequence can be between 2 to 100, 10 to 90, 20 to 80,30 to 70, 40 to 60, or about 50 nucleotides in length. A population ofbarcodes can comprise barcodes of the same length or of differentlengths.

The tag can comprise a double-stranded fixed reference sequencedownstream of the identifier or molecular barcode. Alternatively, thetag can comprise a double-stranded fixed reference sequence upstream ordownstream of the identifier or molecular barcode. Each strand of adouble-stranded fixed reference sequence can be, for example, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, 50 nucleotides in length.

Tagging disclosed herein can be performed using any method. Apolynucleotide can be tagged with an adaptor by hybridization. Forexample, the adaptor can have a nucleotide sequence that iscomplementary to at least a portion of a sequence of the polynucleotide.As an alternative, a polynucleotide can be tagged with an adaptor byligation.

The barcodes or tags can be attached using a variety of techniques.Attachment can be performed by methods including, for example, ligation(blunt-end or sticky-end) or annealing-optimized molecular-inversionprobes. For example, tagging can comprise using one or more enzymes. Theenzyme can be a ligase. The ligase can be a DNA ligase. For example, theDNA ligase can be a T4 DNA ligase, E. coli DNA ligase, and/or mammalianligase. The mammalian ligase can be DNA ligase I, DNA ligase III, or DNAligase IV. The ligase can also be a thermostable ligase. Tags can beligated to a blunt-end of a polynucleotide (blunt-end ligation).Alternatively, tags can be ligated to a sticky end of a polynucleotide(sticky-end ligation). Efficiency of ligation can be increased byoptimizing various conditions. Efficiency of ligation can be increasedby optimizing the reaction time of ligation. For example, the reactiontime of ligation can be less than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, or 20 hours. In a particular example,reaction time of ligation is less than 20 hours. Efficiency of ligationcan be increased by optimizing the ligase concentration in the reaction.For example, the ligase concentration can be at least 10, 50, 100, 150,200, 250, 300, 400, 500, or 600 units/microliter. Efficiency can also beoptimized by adding or varying the concentration of an enzyme suitablefor ligation, enzyme cofactors or other additives, and/or optimizing atemperature of a solution having the enzyme. Efficiency can also beoptimized by varying the addition order of various components of thereaction. The end of tag sequence can comprise dinucleotide to increaseligation efficiency. When the tag comprises a non-complementary portion(e.g., Y-shaped adaptor), the sequence on the complementary portion ofthe tag adaptor can comprise one or more selected sequences that promoteligation efficiency. Such sequences are located at the terminal end ofthe tag. Such sequences can comprise 1, 2, 3, 4, 5, or 6 terminal bases.Reaction solution with high viscosity (e.g., a low Reynolds number) canalso be used to increase ligation efficiency. For example, solution canhave a Reynolds number less than 3000, 2000, 1000, 900, 800, 700, 600,500, 400, 300, 200, 100, 50, 25, or 10. It is also contemplated thatroughly unified distribution of fragments (e.g., tight standarddeviation) can be used to increase ligation efficiency. For example, thevariation in fragment sizes can vary by less than 20%, 15%, 10%, 5%, or1%. Tagging can also comprise primer extension, for example, bypolymerase chain reaction (PCR). Tagging can also comprise any ofligation-based PCR, multiplex PCR, single strand ligation, or singlestrand circularization. Efficiency of tagging (e.g., by ligation) can beincreased to an efficiency of tagging molecules (conversion efficiency)of at least 20%, at least 30%, at least 40%, at least 50%, at least 60%,at least 70%, at least 80%, at least 90%, at least 95%, or at least 98%.

A ligation reaction may be performed in which parent polynucleotides ina sample are admixed with a reaction mixture comprising y differentbarcode oligonucleotides, wherein y=a square root of n. The ligation canresult in the random attachment of barcode oligonucleotides to parentpolynucleotides in the sample. The reaction mixture can then beincubated under ligation conditions sufficient to effect ligation ofbarcode oligonucleotides to parent polynucleotides of the sample. Insome embodiments, random barcodes selected from the y different barcodeoligonucleotides are ligated to both ends of parent polynucleotides.Random ligation of the y barcodes to one or both ends of the parentpolynucleotides can result in production of y² unique identifiers. Forexample, a sample comprising about 10,000 haploid human genomeequivalents of cfDNA can be tagged with about 36 unique identifiers. Theunique identifiers can comprise six unique DNA barcodes. Ligation of 6unique barcodes to both ends of a polynucleotide can result in 36possible unique identifiers produced.

In some embodiments, a sample comprising about 10,000 haploid humangenome equivalents of DNA is tagged with a number of unique identifiersproduced by ligation of a set of unique barcodes to both ends of parentpolynucleotides. For example, 64 unique identifiers can be produced byligation of 8 unique barcodes to both ends of parent polynucleotides.Likewise, 100 unique identifiers can be produced by ligation of 10unique barcodes to both ends of parent polynucleotides, 225 uniqueidentifiers can be produced by ligation of 15 unique barcodes to bothends of parent polynucleotides, 400 unique identifiers can be producedby ligation of 20 unique barcodes to both ends of parentpolynucleotides, 625 unique identifiers can be produced by ligation of25 unique barcodes to both ends of parent polynucleotides, 900 uniqueidentifiers can be produced by ligation of 30 unique barcodes to bothends of parent polynucleotides, 1225 unique identifiers can be producedby ligation of 35 unique barcodes to both ends of parentpolynucleotides, 1600 unique identifiers can be produced by ligation of40 unique barcodes to both ends of parent polynucleotides, 2025 uniqueidentifiers can be produced by ligation of 45 unique barcodes to bothends of parent polynucleotides, and 2500 unique identifiers can beproduced by ligation of 50 unique barcodes to both ends of parentpolynucleotides. The ligation efficiency of the reaction can be over10%, over 20%, over 30%, over 40%, over 50%, over 60%, over 70%, over80%, or over 90%. The ligation conditions can comprise use ofbi-directional adaptors that can bind either end of the fragment andstill be amplifiable. The ligation conditions can comprise sticky-endligation adapters each having an overhang of at least one nucleotidebase. In some instances, the ligation conditions can comprise adaptershaving different base overhangs to increase ligation efficiency. As anon-limiting example, the ligation conditions may comprise adapters withsingle-base cytosine (C) overhangs (i.e., C-tailed adaptors),single-base thymine (T) overhangs (T-tailed adaptors), single-baseadenine (A) overhangs (A-tailed adaptors), and/or single-base guanine(G) overhangs (G-tailed adaptors). The ligation conditions can compriseblunt end ligation, as opposed to tailing. The ligation conditions cancomprise careful titration of an amount of adapter and/or barcodeoligonucleotides. The ligation conditions can comprise the use of over2×, over 5×, over 10×, over 20×, over 40×, over 60×, over 80×, (e.g.,˜100×) molar excess of adapter and/or barcode oligonucleotides ascompared to an amount of parent polynucleotide fragments in the reactionmixture. The ligation conditions can comprise use of a T4 DNA ligase(e.g., NEBNExt Ultra Ligation Module). In an example, 18 microliters ofligase master mix is used with 90 microliter ligation (18 parts of the90) and ligation enhancer. Accordingly, tagging parent polynucleotideswith n unique identifiers can comprise use of a number y differentbarcodes, wherein y=a square root of n. Samples tagged in such a way canbe those with a range of about 10 ng to any of about 100 ng, about 200ng, about 300 ng, about 400 ng, about 500 ng, about 1 or about 10 μg offragmented polynucleotides, e.g., genomic DNA, e.g. cfDNA. The number yof barcodes used to identify parent polynucleotides in a sample candepend on the amount of nucleic acid in the sample.

One method of increasing conversion efficiency involves using a ligaseengineered for optimal reactivity on single-stranded DNA, such as aThermoPhage single-stranded DNA (ssDNA) ligase derivative. Such ligasesbypass traditional steps in library preparation of end-repair andA-tailing that can have poor efficiencies and/or accumulated losses dueto intermediate cleanup steps, and allows for twice the probability thateither the sense or anti-sense starting polynucleotide will be convertedinto an appropriately tagged polynucleotide. It also convertsdouble-stranded polynucleotides that may possess overhangs that may notbe sufficiently blunt-ended by the typical end-repair reaction. Optimalreactions conditions for this ssDNA reaction are: 1× reaction buffer (50millimolar (mM) MOPS (pH 7.5), 1 mM DTT, 5 mM MgCl2, 10 mM KCl). With 50mM ATP, 25 mg/ml BSA, 2.5 mM MnCl2, 200 pmol 85 nt ssDNA oligomer and 5U ssDNA ligase incubated at 65° C. for 1 hour. Subsequent amplificationusing PCR can further convert the tagged single-stranded library to adouble-stranded library and yield an overall conversion efficiency ofwell above 20%. Other methods of increasing conversion rate, e.g., toabove 10%, include, for example, any of the following, alone or incombination: annealing-optimized molecular-inversion probes, blunt-endligation with a well-controlled polynucleotide size range, selection ofa high-efficiency polymerase, sticky-end ligation or an upfrontmultiplex amplification step with or without the use of fusion primers,optimization of end bases in a target sequence, optimization of reactionconditions (including reaction time), and the introduction of one ormore steps to clean up a reaction (e.g., of unwanted nucleic acidfragments) during the ligation, and optimization of temperature ofbuffer conditions. Sticky end ligation may be performed usingmultiple-nucleotide overhangs. Sticky end ligation may be performedusing single-nucleotide overhangs comprising an A, T, C, or G bases.

The present disclosure also provides compositions of taggedpolynucleotides. The polynucleotides can comprise fragmented DNA, e.g.cfDNA. A set of polynucleotides in the composition that map to amappable base position in a genome can be non-uniquely tagged, that is,the number of different identifiers can be at least at least 2 and fewerthan the number of polynucleotides that map to the mappable baseposition. A composition of between about 10 ng to about 10 μg (e.g., anyof about 10 ng-1 about 10 ng-100 ng, about 100 ng-10 μg, about 100 ng-1μg, about 1 μg-10 μg) can bear between any of 2, 5, 10, 50 or 100 to anyof 100, 1000, 10,000 or 100,000 different identifiers. For example,between 5 and 100 different identifiers can be used to tag thepolynucleotides in such a composition.

Sequencing

Tagged polynucleotides can be sequenced to generate sequence reads. Forexample, a tagged duplex polynucleotide can be sequenced. Sequence readscan be generated from only one strand of a tagged duplex polynucleotide.Alternatively, both strands of a tagged duplex polynucleotide cangenerate sequence reads. The two strands of the tagged duplexpolynucleotide can comprise the same tags. Alternatively, the twostrands of the tagged duplex polynucleotide can comprise different tags.When the two strands of the tagged duplex polynucleotide are differentlytagged, sequence reads generated from one strand (e.g., a Watson strand)can be distinguished from sequence reads generated from the otherstrands (e.g., a Crick strand). Sequencing can involve generatingmultiple sequence reads for each molecule. This occurs, for example, asa result the amplification of individual polynucleotide strands duringthe sequencing process, e.g., by PCR.

Methods disclosed herein can comprise amplifying of polynucleotides.Amplification can be performed before tagging, after tagging, or both.Polynucleotides amplification can result in the incorporation ofnucleotides into a nucleic acid molecule or primer thereby forming a newnucleic acid molecule complementary to a template nucleic acid. Thenewly formed polynucleotide molecule and its template can be used astemplates to synthesize additional polynucleotides. The polynucleotidesbeing amplified can be any nucleic acids, for example, deoxyribonucleicacids, including genomic DNAs, cDNAs (complementary DNA), cfDNAs, andcirculating tumor DNAs (ctDNAs). The polynucleotides being amplified canalso be RNAs. As used herein, one amplification reaction may comprisemany rounds of DNA replication. DNA amplification reactions can include,for example, polymerase chain reaction (PCR). One PCR reaction maycomprise 2-100 “cycles” of denaturation, annealing, and synthesis of aDNA molecule. For example, 2-7, 5-10, 6-11, 7-12, 8-13, 9-14, 10-15,11-16, 12-17, 13-18, 14-19, or 15-20 cycles can be performed during theamplification step. The condition of the PCR can be optimized based onthe GC content of the sequences, including the primers. Amplificationprimers can be chosen to select for a target sequence of interest.Primers can be designed to optimize or maximize conversion efficiency.In some embodiments, primers contain a short sequence between theprimers so as to pull out a small region of interest. In someembodiments, primers target nucleosomal regions so that the primershybridize to areas where nucleosomes are present, as opposed to areasbetween nucleosomes, because inter-nucleosomal areas are more highlycleaved and therefore less likely to be present as targets.

In some embodiments, regions of the genome are targeted that aredifferentially protected by nucleosomes and other regulatory mechanismsin cancer cells, the tumor microenvironment, or immune system components(granulocytes, tumor infiltrating lymphocytes, etc). In someembodiments, other regions are targeted that are stable and/or notdifferentially regulated in tumor cells. Within these regions,differences in coverage, cleavage sites, fragment length, sequencecontent, sequence content at fragment endpoints, or sequence content ofthe nearby genomic context can be used to infer the presence or absenceof a certain classification of cancer cells (e.g., EGFR mutant, KRASmutant, ERBb2 amplified, or PD-1 expression cancers), or type of cancer(e.g., lung adenocarcinoma, breast, or colorectal cancer). Suchtargeting can also enhance the sensitivity and/or specificity of theassay by enhancing coverage at certain sites or the probability ofcapture. These principles apply to methods of targeting including, butnot limited to, ligation plus hybrid capture-based enrichment,amplification-based enrichment, rolling circle-based enrichment withsequence/genomic location specific initiation primers, and othermethods. The regions that can be targeted with such methods andsubsequent analysis include, but are not limited to, intronic regions,exonic regions, promoter regions, TSS regions, distant regulatoryelements, enhancer regions, and super-enhancer regions and/or junctionsof the preceding. These methods can also be used to infer the tissue oforigin of the tumor and/or a measure of tumor burden in combination withother techniques described herein for determining variants (e.g.,germline or somatic variants) contained within the sample. For example,germline variants can determine predisposition for certain types ofcancer, while somatic variants can correlate to certain types of cancerspecifically based on the affected genes, pathways and percentages ofthe variants. This information can then be used in combination withepigenetic signatures relating to regulatory mechanisms and/or chemicalmodifications such as, for example, methylation, hydroxymethylation,acetylation, and/or RNA. The nucleic acid library can involve combinedanalysis of DNA, DNA modifications and RNA to enhance sensitivity andspecificity to the detection of cancer, type of cancer, molecularpathways activated in the specific disease, tissue of origin as well asa measure that corresponds to tumor burden. Approaches for analyzingeach of the above have been outlined elsewhere and can be combined foranalysis of a single or multiple samples from the same patient, wherebythe sample can be derived from various bodily specimens.

Nucleic acid amplification techniques can be used with the assaysdescribed herein. Some amplification techniques are the PCRmethodologies which can include, but are not limited to, solution PCRand in situ PCR. For example, amplification may comprise PCR-basedamplification. Alternatively, amplification may comprise non PCR-basedamplification. Amplification of the template nucleic acid may compriseuse of one or more polymerases. For example, the polymerase may be a DNApolymerase or an RNA polymerase. In some cases, high fidelityamplification is performed such as with the use of high fidelitypolymerase (e.g., Phusion RTM High-Fidelity DNA Polymerase) or PCRprotocols. In some cases, the polymerase may be a high fidelitypolymerase. For example, the polymerase may be KAPA HiFi DNA polymerase.The polymerase may also be Phusion DNA polymerase or an Ultra IIpolymerase. The polymerase may be used under reaction conditions thatreduce or minimize amplification biases, e.g., due to fragment lengthand/or GC content.

Amplification of a single strand of a polynucleotide by PCR willgenerate copies both of that strand and its complement. Duringsequencing, both the strand and its complement will generate sequencereads. However, sequence reads generated from the complement of, forexample, the Watson strand, can be identified as such because they bearthe complement of the portion of the duplex tag that tagged the originalWatson strand. In contrast, a sequence read generated from a Crickstrand or its amplification product will bear the portion of the duplextag that tagged the original Crick strand. In this way, a sequence readgenerated from an amplified product of a complement of the Watson strandcan be distinguished from a complement sequence read generated from anamplification product of the Crick strand of the original molecule.

Amplification, such as PCR amplification, is typically performed inrounds. Exemplary rounds of amplification include 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, or more roundsof amplification. Amplification conditions can be optimized, forexample, for buffer conditions and polymerase type and conditions. Theamplification also can be modified to reduce bias in the sampleprocessing, for example, by reducing non-specific amplification bias, GCcontent bias, and size bias.

In some embodiments, sequences can be enriched prior to sequencing.Enrichment can be performed for specific target regions ornonspecifically. In some embodiments, targeted genomic regions ofinterest may be enriched with capture probes (“baits”) selected for oneor more bait set panels using a differential tiling and capture scheme.A differential tiling and capture scheme uses bait sets of differentrelative concentrations to differentially tile (e.g., at different“resolutions”) across genomic regions associated with baits, subject toa set of constraints (e.g., sequencer constraints such as sequencingload, utility of each bait, etc.), and capture them at a desired levelfor downstream sequencing. These targeted genomic regions of interestmay include single-nucleotide variants (SNVs) and indels (i.e.,insertions or deletions). The targeted genomic regions of interest maycomprise backbone genomic regions of interest (“backbone regions”) orhot-spot genomic regions of interest (“hot-spot regions” or “hotspotregions” or “hot-spots” or “hotspots”). While “hotpots” can refer toparticular loci associated with sequence variants, “backbone” regionscan refer to larger genomic regions, each of which can have one or morepotential sequence variants. For example, a backbone region can be aregion containing one or more cancer-associated mutations, while ahotspot can be a locus with a particular mutation associated withrecurring cancer or a locus with a particular recurring mutationassociated with cancer. Both backbone and hot-spot genomic regions ofinterest may comprise tumor-relevant marker genes commonly included inliquid biopsy assays (e.g., BRAF, BRCA 1/2, EGFR, KRAS, PIK3CA, ROS1,TP53, and others), for which one or more variants may be expected to beseen in subjects with cancer. In some embodiments, biotin-labeled beadswith probes to one or more regions of interest can be used to capturetarget sequences, optionally followed by amplification of those regions,to enrich for the regions of interest.

The amount of sequencing data that can be obtained from a sample isfinite, and constrained by such factors as the quality of nucleic acidtemplates, number of target sequences, scarcity of specific sequences,limitations in sequencing techniques, and practical considerations suchas time and expense. Thus, a “read budget” is a way to conceptualize theamount of genetic information that can be extracted from a sample. Aper-sample read budget can be selected that identifies the total numberof base reads to be allocated to a test sample comprising apredetermined amount of DNA in a sequencing experiment. The read budgetcan be based on total reads produced, e.g., including redundant readsproduced through amplification. Alternatively, it can be based on numberof unique molecules detected in the sample. In certain embodiments readbudget can reflect the amount of double-stranded support for a call at alocus. That is, the percentage of loci for which reads from both strandsof a DNA molecule are detected.

Factors of a read budget include read depth and panel length. Forexample, a read budget of 3,000,000,000 reads can be allocated as150,000 bases at an average read depth of 20,000 reads/base. Read depthcan refer to number of molecules producing a read at a locus. In thepresent disclosure, the reads at each base can be allocated betweenbases in the backbone region of the panel, at a first average read depthand bases in the hotspot region of the panel, at a deeper read depth. Insome embodiments, a sample is sequenced to a read depth determined bythe amount of nucleic acid present in a sample. In some embodiments, asample is sequenced to a set read depth, such that samples comprisingdifferent amounts of nucleic acid are sequenced to the same read depth.For example, a sample comprising 300 ng of nucleic acids can besequenced to a read depth 1/10 that of a sample comprising 30 ng ofnucleic acids. In some embodiments, nucleic acids from two or moredifferent subjects can be added together at a ratio based on the amountof nucleic acids obtained from each of the subjects.

By way of non-limiting example, if a read budget consists of 100,000read counts for a given sample, those 100,000 read counts will bedivided between reads of backbone regions and reads of hotspot regions.Allocating a large number of those reads (e.g., 90,000 reads) tobackbone regions will result in a small number of reads (e.g., theremaining 10,000 reads) being allocated to hotspot regions. Conversely,allocating a large number of reads (e.g., 90,000 reads) to hotspotregions will result in a small number of reads (e.g., the remaining10,000 reads) being allocated to backbone regions. Thus, a skilledworker can allocate a read budget to provide desired levels ofsensitivity and specificity. In certain embodiments, the read budget canbe between 100,000,000 reads and 100,000,000,000 reads, e.g., between500,000,000 reads and 50,000,000,000 reads, or between about1,000,000,000 reads and 5,000,000,000 reads across, for example, 20,000bases to 100,000 bases.

All polynucleotides (e.g., amplified polynucleotides) can be submittedto a sequencing device for sequencing. Alternatively, a sampling, orsubset, of all of the amplified polynucleotides is submitted to asequencing device for sequencing. With respect to any originaldouble-stranded polynucleotide there can be three results with respectto sequencing. First, sequence reads can be generated from bothcomplementary strands of the original molecule (that is, from both theWatson strand and from the Crick strand). Second, sequence reads can begenerated from only one of the two complementary strands (that is,either from the Watson strand or from the Crick strand, but not both).Third, no sequence read may be generated from either of the twocomplementary strands. Consequently, counting unique sequence readsmapping to a genetic locus will underestimate the number ofdouble-stranded polynucleotides in the original sample mapping to thelocus. Described herein are methods of estimating the unseen anduncounted polynucleotides.

The sequencing method can be massively parallel sequencing, that is,simultaneously (or in rapid succession) sequencing any of at least 100,1000, 10,000, 100,000, 1 million, 10 million, 100 million, or 1 billionpolynucleotide molecules.

Sequencing methods may include, but are not limited to: high-throughputsequencing, pyrosequencing, sequencing-by-synthesis, single-moleculesequencing, nanopore sequencing, semiconductor sequencing,sequencing-by-ligation, sequencing-by-hybridization, RNA-Seq (Illumina),Digital Gene Expression (Helicos), Next generation sequencing, SingleMolecule Sequencing by Synthesis (SMSS) (Helicos), massively-parallelsequencing, Clonal Single Molecule Array (Solexa), shotgun sequencing,Maxam-Gilbert or Sanger sequencing, primer walking, sequencing usingPacBio, SOLiD, Ion Torrent, or Nanopore platforms and any othersequencing methods known in the art.

The method can comprise sequencing at least 1 million, 10 million, 100million, 500 million, 1 billion, 1.1 billion, 1.2 billion, 1.5 billion,2 billion, 2.5 billion, 3 billion, 3.5 billion, 4 billion, 4.5 billion,5 billion, 5.5 billion, 6 billion, 6.5 billion, 7 billion, 8 billion, 9billion or 10 billion base pairs. In some cases, the methods cancomprise sequencing from about 1 billion to about 7 billion, from about1.1 billion to about 6.8 billion, from about 1.2 billion, to about 6.5billion, from about 1.1 billion to about 6.4 billion, from about 1.5billion to about 7 billion, from about 2 billion to about 6 billion,from about 2.5 billion to about 5.5 billion, from about 3 billion toabout 5 billion base pairs. For example, the methods can comprisesequencing from about 1.2 billion, to about 6.5 billion base pairs.

Tumor Markers

A tumor marker is a genetic variant associated with one or more cancers.Tumor markers may be determined using any of several resources ormethods. A tumor marker may have been previously discovered or may bediscovered de novo using experimental or epidemiological techniques.Detection of a tumor marker may be indicative of cancer when the tumormarker is highly correlated a cancer. Detection of a tumor marker may beindicative of cancer when a tumor marker in a region or gene occur witha frequency that is greater than a frequency for a given backgroundpopulation or dataset.

Publicly available resources such as scientific literature and databasesmay describe in detail genetic variants found to be associated withcancer. Scientific literature may describe experiments or genome-wideassociation studies (GWAS) associating one or more genetic variants withcancer. Databases may aggregate information gleaned from sources such asscientific literature to provide a more comprehensive resource fordetermining one or more tumor markers. Non-limiting examples ofdatabases include FANTOM, GTex, GEO, Body Atlas, INSiGHT, OMIM (OnlineMendelian Inheritance in Man, omim.org), cBioPortal (cbioportal.org),CIViC (Clinical Interpretations of Variants in Cancer,civic.genome.wustl.edu), DOCM (Database of Curated Mutations,docm.genome.wustl.edu), and ICGC Data Portal (dcc.icgc.org). In afurther example, the COSMIC (Catalogue of Somatic Mutations in Cancer)database allows for searching of tumor markers by cancer, gene, ormutation type. Tumor markers may also be determined de novo byconducting experiments such as case control or association (e.g,genome-wide association studies) studies.

One or more tumor markers may be detected in the sequencing panel. Atumor marker may be one or more genetic variants associated with cancer.Tumor markers can be selected from single nucleotide variants (SNVs),copy number variants (CNVs), insertions or deletions (e.g., indels),gene fusions and inversions. Tumor markers may affect the level of aprotein. Tumor markers may be in a promoter or enhancer, and may alterthe transcription of a gene. The tumor markers may affect thetranscription and/or translation efficacy of a gene. The tumor markersmay affect the stability of a transcribed mRNA. The tumor marker mayresult in a change to the amino acid sequence of a translated protein.The tumor marker may affect splicing, may change the amino acid coded bya particular codon, may result in a frameshift, or may result in apremature stop codon. The tumor marker may result in a conservativesubstitution of an amino acid. One or more tumor markers may result in aconservative substitution of an amino acid. One or more tumor markersmay result in a nonconservative substitution of an amino acid.

One or more of the tumor markers may be a driver mutation. A drivermutation is a mutation that gives a selective advantage to a tumor cellin its microenvironment, through either increasing its survival orreproduction. None of the tumor markers may be a driver mutation. One ormore of the tumor markers may be a passenger mutation. A passengermutation is a mutation that has no effect on the fitness of a tumor cellbut may be associated with a clonal expansion because it occurs in thesame genome with a driver mutation.

The frequency of a tumor marker may be as low as 0.001%. The frequencyof a tumor marker may be as low as 0.005%. The frequency of a tumormarker may be as low as 0.01%. The frequency of a tumor marker may be aslow as 0.02%. The frequency of a tumor marker may be as low as 0.03%.The frequency of a tumor marker may be as low as 0.05%. The frequency ofa tumor marker may be as low as 0.1%. The frequency of a tumor markermay be as low as 1%.

No single tumor marker may be present in more than 50%, of subjectshaving the cancer. No single tumor marker may be present in more than40%, of subjects having the cancer. No single tumor marker may bepresent in more than 30%, of subjects having the cancer. No single tumormarker may be present in more than 20%, of subjects having the cancer.No single tumor marker may be present in more than 10%, of subjectshaving the cancer. No single tumor marker may be present in more than5%, of subjects having the cancer. A single tumor marker may be presentin 0.001% to 50% of subjects having cancer. A single tumor marker may bepresent in 0.01% to 50% of subjects having cancer. A single tumor markermay be present in 0.01% to 30% of subjects having cancer. A single tumormarker may be present in 0.01% to 20% of subjects having cancer. Asingle tumor marker may be present in 0.01% to 10% of subjects havingcancer. A single tumor marker may be present in 0.1% to 10% of subjectshaving cancer. A single tumor marker may be present in 0.1% to 5% ofsubjects having cancer.

Detection of a tumor marker may indicate the presence of one or morecancers. Detection may indicate presence of a cancer selected from thegroup comprising ovarian cancer, pancreatic cancer, breast cancer,colorectal cancer, non-small cell lung carcinoma (e.g., squamous cellcarcinoma, or adenocarcinoma) or any other cancer. Detection mayindicate the presence of any cancer selected from the group comprisingovarian cancer, pancreatic cancer, breast cancer, colorectal cancer,non-small cell lung carcinoma (squamous cell or adenocarcinoma) or anyother cancer. Detection may indicate the presence of any of a pluralityof cancers selected from the group comprising ovarian cancer, pancreaticcancer, breast cancer, colorectal cancer and non-small cell lungcarcinoma (squamous cell or adenocarcinoma), or any other cancer.Detection may indicate presence of one or more of any of the cancersmentioned in this application.

One or more cancers may exhibit a tumor marker in at least one exon inthe panel. One or more cancers selected from the group comprisingovarian cancer, pancreatic cancer, breast cancer, colorectal cancer,non-small cell lung carcinoma (squamous cell or adenocarcinoma), or anyother cancer, each exhibit a tumor marker in at least one exon in thepanel. Each of at least 3 of the cancers may exhibit a tumor marker inat least one exon in the panel. Each of at least 4 of the cancers mayexhibit a tumor marker in at least one exon in the panel. Each of atleast 5 of the cancers may exhibit a tumor marker in at least one exonin the panel. Each of at least 8 of the cancers may exhibit a tumormarker in at least one exon in the panel. Each of at least 10 of thecancers may exhibit a tumor marker in at least one exon in the panel.All of the cancers may exhibit a tumor marker in at least one exon inthe panel.

If a subject has a cancer, the subject may exhibit a tumor marker in atleast one exon or gene in the panel. At least 85% of subjects having acancer may exhibit a tumor marker in at least one exon or gene in thepanel. At least 90%, of subjects having a cancer may exhibit a tumormarker in at least one exon or gene in the panel. At least 92% ofsubjects having a cancer may exhibit a tumor marker in at least one exonor gene in the panel. At least 95% of subjects having a cancer mayexhibit a tumor marker in at least one exon or gene in the panel. Atleast 96% of subjects having a cancer may exhibit a tumor marker in atleast one exon or gene in the panel. At least 97% of subjects having acancer may exhibit a tumor marker in at least one exon or gene in thepanel. At least 98% of subjects having a cancer may exhibit a tumormarker in at least one exon or gene in the panel. At least 99% ofsubjects having a cancer may exhibit a tumor marker in at least one exonor gene in the panel. At least 99.5% of subjects having a cancer mayexhibit a tumor marker in at least one exon or gene in the panel.

If a subject has a cancer, the subject may exhibit a tumor marker in atleast one region in the panel. At least 85% of subjects having a cancermay exhibit a tumor marker in at least one region in the panel. At least90%, of subjects having a cancer may exhibit a tumor marker in at leastone region in the panel. At least 92% of subjects having a cancer mayexhibit a tumor marker in at least one region in the panel. At least 95%of subjects having a cancer may exhibit a tumor marker in at least oneregion in the panel. At least 96% of subjects having a cancer mayexhibit a tumor marker in at least one region in the panel. At least 97%of subjects having a cancer may exhibit a tumor marker in at least oneregion in the panel. At least 98% of subjects having a cancer mayexhibit a tumor marker in at least one region in the panel. At least 99%of subjects having a cancer may exhibit a tumor marker in at least oneregion in the panel. At least 99.5% of subjects having a cancer mayexhibit a tumor marker in at least one region in the panel.

Detection may be performed with a high sensitivity and/or a highspecificity. Sensitivity can refer to a measure of the proportion ofpositives that are correctly identified as such. In some cases,sensitivity refers to the percentage of all existing tumor markers thatare detected. In some cases, sensitivity refers to the percentage ofsick people who are correctly identified as having certain disease.Specificity can refer to a measure of the proportion of negatives thatare correctly identified as such. In some cases, specificity refers tothe proportion of unaltered bases which are correctly identified. Insome cases, specificity refers to the percentage of healthy people whoare correctly identified as not having certain disease. The non-uniquetagging method described previously significantly increases specificityof detection by reducing noise generated by amplification and sequencingerrors, which reduces frequency of false positives. Detection may beperformed with a sensitivity of at least 95%, 97%, 98%, 99%, 99.5%, or99.9% and/or a specificity of at least 80%, 90%, 95%, 97%, 98% or 99%.Detection may be performed with a sensitivity of at least 90%, 95%, 97%,98%, 99%, 99.5%, 99.6%, 99.98%, 99.9% or 99.95%. Detection may beperformed with a specificity of at least 90%, 95%, 97%, 98%, 99%, 99.5%,99.6%, 99.98%, 99.9% or 99.95%. Detection may be performed with aspecificity of at least 70% and a sensitivity of at least 70%, aspecificity of at least 75% and a sensitivity of at least 75%, aspecificity of at least 80% and a sensitivity of at least 80%, aspecificity of at least 85% and a sensitivity of at least 85%, aspecificity of at least 90% and a sensitivity of at least 90%, aspecificity of at least 95% and a sensitivity of at least 95%, aspecificity of at least 96% and a sensitivity of at least 96%, aspecificity of at least 97% and a sensitivity of at least 97%, aspecificity of at least 98% and a sensitivity of at least 98%, aspecificity of at least 99% and a sensitivity of at least 99%, or aspecificity of 100% a sensitivity of 100%. In some cases, the methodscan detect a tumor marker at a sensitivity of sensitivity of about 80%or greater. In some cases, the methods can detect a tumor marker at asensitivity of sensitivity of about 95% or greater. In some cases, themethods can detect a tumor marker at a sensitivity of sensitivity ofabout 80% or greater, and a sensitivity of sensitivity of about 95% orgreater.

Detection may be highly accurate. Accuracy may apply to theidentification of tumor markers in cell free DNA, and/or to thediagnosis of cancer. Statistical tools, such as co-variate analysisdescribed above, may be used to increase and/or measure accuracy. Themethods can detect a tumor marker at an accuracy of at least 80%, 90%,95%, 97%, 98% or 99%, 99.5%, 99.6%, 99.98%, 99.9%, or 99.95%. In somecases, the methods can detect a tumor marker at an accuracy of at least95% or greater.

Detection Limit/Noise Range

Noise can be introduced through errors in copying and/or reading apolynucleotide. For example, in a sequencing process, a singlepolynucleotide can first be subject to amplification. Amplification canintroduce errors, so that a subset of the amplified polynucleotides maycontain, at a particular locus, a base that is not the same as theoriginal base at that locus. Furthermore, in the reading process a baseat any particular locus may be read incorrectly. As a consequence, thecollection of sequence reads can include a certain percentage of basecalls at a locus that are not the same as the original base. In typicalsequencing technologies this error rate can be in the single digits,e.g., 2%-3%. In some instances, the error rate can be up to about 10%,up to about 9%, up to about 8%, up to about 7%, up to about 6%, up toabout 5%, up to about 4%, up to about 3%, up to about 2%, or up to about1%. When a collection of molecules that are all presumed to have thesame sequence are sequenced, this noise may be sufficiently small thatone can identify the original base with high reliability.

However, if a collection of parent polynucleotides includes a subset ofpolynucleotides that vary at a particular locus, noise can be asignificant problem. This can be the case, for example, when cell-freeDNA includes not only germline DNA, but DNA from another source, such asfetal DNA or DNA from a cancer cell. In this case, if the frequency ofmolecules with sequence variants may be in the same range as thefrequency of errors introduced by the sequencing process, then truesequence variants may not be distinguishable from noise. This mayinterfere, for example, with detecting sequence variants in a sample.For example, sequences can have a per-base error rate of 0.5-1%.Amplification bias and sequencing errors introduce noise into the finalsequencing product. This noise can diminish sensitivity of detection. Asa non-limiting example, sequence variants whose frequency is less thanthe sequencing error rate can be mistaken for noise.

A noise range or detection limit refers to instances where the frequencyof molecules with sequence variants is in the same range as thefrequency of errors introduced by the sequencing process. A “detectionlimit” may also refer to instances where too few variant-carryingmolecules are sequenced for the variant to be detected. The frequency ofmolecules with sequence variants may be in the same range as thefrequency of errors as a result of a small amount of nucleic acidmolecules. As a non-limiting example, a sampled amount of nucleic acids,e.g. 100 ng, may contain a relatively small number of cell-free nucleicacid molecules, e.g. circulating tumor DNA molecules, such that thefrequency of a sequence variant may be low, even though the variant maybe present in a majority of circulating tumor DNA molecules.Alternately, the sequence variant may be rare or occur in only a verysmall amount of the sampled nucleic acids such that a detected variantis indistinguishable from noise and/or sequencing error. As anon-limiting example, at a particular locus, a tumor marker may only bedetected in 0.1% to 5% of all reads at that locus.

Distortion can be manifested in the sequencing process as a differencein signal strength, e.g., total number of sequence reads, produced bymolecules in a parent population at the same frequency. Distortion canbe introduced, for example, through amplification bias, GC bias, orsequencing bias. This may interfere with detecting copy number variationin a sample. GC bias results in the uneven representation of areas richor poor in GC content in the sequence reading. Also, by providing readsof sequences in greater or less amounts than their actual number in apopulation, amplification bias can distort measurements of copy numbervariation.

One way to reduce noise and/or distortion from a single individualmolecule or from an ensemble of molecules is to group sequence readsinto families derived from original individual molecules to reduce noiseand/or distortion from a single individual molecule or from an ensembleof molecules. Efficient conversion of individual polynucleotides in asample of initial genetic material into sequence-ready tagged parentpolynucleotides may increase the probability that individualpolynucleotides in a sample of initial genetic material will berepresented in a sequence-ready sample. This can produce sequenceinformation about more polynucleotides in the initial sample.Additionally, high yield generation of consensus sequences for taggedparent polynucleotides by high-rate sampling of progeny polynucleotidesamplified from the tagged parent polynucleotides, and collapsing ofgenerated sequence reads into consensus sequences representing sequencesof parent tagged polynucleotides can reduce noise introduced byamplification bias and/or sequencing errors, and can increasesensitivity of detection. Collapsing sequence reads into a consensussequence is one way to reduce noise in the received message from onemolecule. Using probabilistic functions that convert receivedfrequencies into likelihood or posterior estimates of each of thepossible true nucleotides using defined estimates of amplification andsequencing error profiles is another way to reduce noise and/ordistortion. With respect to an ensemble of molecules, grouping readsinto families and determining a quantitative measure of the familiesreduces distortion, for example, in the quantity of molecules at each ofa plurality of different loci. Again, collapsing sequence reads ofdifferent families into consensus sequences eliminate errors introducedby amplification and/or sequencing error. Furthermore, determiningfrequencies of base calls based on probabilities derived from familyinformation also reduces noise in the received message from an ensembleof molecules. Frequency reporting or tumor marker calls also can be madeusing a plurality of reference sequences and coverage observations, fromwhich a frequency for observing a tumor marker at a position will bedetermined. Reference sequences can comprise sequences or markerprofiles from healthy individuals or from individuals having a diseaseor condition, such as cancer. A frequency from “known” reference samplescan be used to set a threshold frequency for making a marker detectioncall. For example, a frequency of 0.1% for a nucleotide having an “A” ata certain position can be used as a threshold for determining whether ornot to call a base at that position “A” in a test subject. For example,at least 20, at least 50, at least 100, at least 500, at least 1,000, atleast 2,000, at least 3,000, at least 4,000, at least 5,000, at least6,000, at least 7,000, at least 8,000, at least 9,000, at least 10,000,at least 11,000, at least 12,000, at least 13,000, at least 14,000, atleast 15,000, at least 16,000, at least 17,000, at least 18,000, atleast 19,000, at least 20,000, at least 30,000, at least 40,000, atleast 50,000, at least 60,000, at least 70,000, at least 80,000, atleast 90,000, or at least 100,000 reference sequences can be used.

Noise and/or distortion may be further reduced by identifyingcontaminating molecules from other processed samples by comparingmolecule tagging and location information to a collection of observedmolecules within the sample being processed or across batches ofsamples. Noise and/or distortion may be further reduced by comparinggenetic variations in a sequence read with genetic variations othersequence reads. A genetic variation observed in one sequence read andagain in other sequence reads increases the probability that a detectedvariant is in fact a tumor marker and not merely a sequencing error ornoise. As a non-limiting example, if a genetic variation is observed ina first sequence read and also observed in a second sequence read, aBayesian inference may be made regarding whether the variation is infact a genetic variation and not a sequencing error.

Repeated detection of a variant may increase the probability,likelihood, and/or confidence that a variant is accurately detected. Avariant can be repeatedly detected by comparing two or more sets ofgenetic data or genetic variations. The two or more sets of geneticvariations can be detected in both samples at multiple time points anddifferent samples at the same time point (for example a re-analyzedblood sample). In detecting a variant in the noise range or below thenoise threshold, the re-sampling or repeated detection of a lowfrequency variant makes it more likely that the variant is in fact avariant and not a sequencing error. Re-sampling can be from the samesample, such as a sample that is re-analyzed or re-run, or from samplesat different time points.

Co-variate detection may increase the probability, likelihood, and/orconfidence that a variant is accurately detected. For co-variate tumormarkers, the presence of one tumor marker is associated with thepresence of one or more other tumor markers. Based on the detection of aco-variate genetic variation, it may be possible to infer the presenceof an associated co-variate genetic variation, even where the associatedgenetic variation is present below a detection limit. Alternately, basedon the detection of a co-variate genetic variation, the diagnosticconfidence indication for the associated genetic variation may beincreased. Further, in some instances where a co-variate variant isdetected, a detection threshold for a co-variate variant detected belowa detection limit may be decreased. Non-limiting examples of co-variatevariations or genes include: driver mutations and resistance mutations,driver mutations and passenger mutations. As specific example ofco-variants or genes is EGFR L858R activating mutation and EGFR T790Mresistance mutation, found in lung cancers. Numerous other co-variatevariants and genes are associated with various resistance mutations andwill be recognized by one having skill in the art.

In one implementation, using measurements from a plurality of samplescollected substantially at once or over a plurality of time points, thediagnostic confidence indication for each variant can be adjusted toindicate a confidence of predicting the observation of the copy numbervariation (CNV) or mutation or tumor marker. The confidence can beincreased by using measurements at a plurality of time points todetermine whether cancer is advancing, in remission or stabilized. Thediagnostic confidence indication can be assigned by any of a number ofstatistical methods and can be based, at least in part, on the frequencyat which the measurements are observed over a period of time. Forexample, a statistical correlation of current and prior results can bedone. Alternatively, for each diagnosis, a hidden Markov model can bebuilt, such that a maximum likelihood or maximum a posteriori decisioncan be made based on the frequency of occurrence of a particular testevent from a plurality of measurements or a time points. As part of thismodel, the probability of error and resultant diagnostic confidenceindication for a particular decision can be output as well. In thismanner, the measurements of a parameter, whether or not they are in thenoise range, may be provided with a confidence interval. Tested overtime, one can increase the predictive confidence of whether a cancer isadvancing, stabilized or in remission by comparing confidence intervalsover time. Two sampling time points can be separated by at least about 1microsecond, 1 millisecond, 1 second, 10 seconds, 30 seconds, 1 minute,10 minutes, 30 minutes, 1 hour, 12 hours, 1 day, 1 week, 2 weeks, 3weeks, one month, or one year. Two time points can be separated by abouta month to about a year, about a year to about 5 years, or no more thanabout three months, two months, one month, three weeks, two weeks, oneweek, one day, or twelve hours. In some embodiments, two time points canbe separated by a therapeutic event such as the administration of atreatment or the performance of a surgical procedure. When the two timepoints are separated by the therapeutic event, CNV or mutations detectedcan be compared before and after the event.

After sequencing data of cell free polynucleotide sequences iscollected, one or more bioinformatics processes may be applied to thesequence data to detect genetic features or variations such as cfDNAcharacteristics at regulatory elements, nucleosomal spacing/nucleosomebinding patterns, chemical modifications of nucleic acids, copy numbervariation, and mutations or changes in epigenetic markers, including butnot limited to methylation profiles, and genetic variants such as SNVs,CNVs, indels, and/or fusions. In some cases, in which copy numbervariation analysis is desired, sequence data may be: 1) aligned with areference genome and mapped to individual molecules; 2) filtered; 4)partitioned into windows or bins of a sequence; 5) coverage reads andmolecules counted for each window; 6) coverage molecules can then benormalized using a statistical modeling algorithm; and 7) an output filecan be generated reflecting discrete copy number states at variouspositions in the genome. In some cases, the number of coveragereads/molecules or normalized coverage reads aligning to a particularlocus of the reference genome is counted. In other cases, in whichmutation analysis is desired, sequence data may be 1) aligned with areference genome and mapped to individual molecules; 2) filtered; 4)frequency of variant bases calculated based on coverage reads for thatspecific base; 5) variant base frequency normalized using a stochastic,statistical or probabilistic modeling algorithm; and 6) an output filecan be generated reflecting mutation states at various positions in thegenome. In some cases, identifiers (such as those including barcodes)can be used to group sequence reads during mutation analysis. In somecases, sequence reads are grouped into families, e.g., by usingidentifiers or a combination of identifiers and start/stop positions orsequences. In some cases, a base call can be made by comparingnucleotides in one or more families to a reference sequence anddetermining the frequency of a particular base 1) within each family,and 2) between the families and the reference sequences. A nucleotidebase call can be made based on criteria such as the percentage offamilies having a base at a position. In some cases, a base call isreported if its frequency is greater than a noise threshold asdetermined by frequency in a plurality of reference sequences (e.g.,sequences from healthy individuals). Temporal information from thecurrent and prior analysis of the patient or subject is used to enhancethe analysis and determination. In some embodiments, sequenceinformation from the patient or subject is compared to sequenceinformation obtained from a cohort of healthy individuals, a cohort ofcancer patients, or germline DNA from the patient or subject. GermlineDNA can be obtained, without limitation, from bodily fluid, whole blood,platelets, serum, plasma, stool, red blood cells, white blood cells orleukocytes, endothelial cells, tissue biopsies, synovial fluid,lymphatic fluid, ascites fluid, interstitial or extracellular fluid, thefluid in spaces between cells, including gingival crevicular fluid, bonemarrow, cerebrospinal fluid, saliva, mucous, sputum, semen, sweat,urine, or any other bodily fluids. A cohort of cancer patients can havethe same type of cancer as the patient or subject, the same stage ofcancer as the patient or subject, both, or neither. In some embodiments,a cohort of cancer patients, a cohort of healthy individuals, orgermline DNA from the subject is used to provide a baseline frequency ofa base at a position, and the baseline frequency is used in making abase call in the subject. Without limitation, a frequency for a base ata position in a cohort of healthy individuals, or germline DNA from thesubject can be compared to the frequency of a base detected amongsequence reads from the subject.

In some embodiments, the methods and systems of the present disclosurecan be used to detect a minor allele frequency (MAF) of 0.025% or lower,0.05% or lower, 0.075% or lower, or 0.1% or lower. Copy number variationcan be measured as a ratio of (1) unique molecule counts (UMCs) for agene in a test sample to (2) UMCs for that gene in a reference sample(e.g., control sample). In some embodiments, the methods and systems ofthe present disclosure can be used to detect a copy number variationthat is a copy number amplification (CNA). In some embodiments, themethods and systems of the present disclosure can be used to detect aCNA of at least 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more. In someembodiments, the methods and systems of the present disclosure can beused to detect a copy number variation that is a copy number loss (CNL).In some embodiments, the methods and systems of the present disclosurecan be used to detect a CNL of less than 0.9, 0.8, 0.7, 0.6, 0.5, 0.4,0.3, 0.2, 0.1, or 0.05.

A variety of different reactions and/operations may occur within thesystems and methods disclosed herein, including but not limited to:nucleic acid sequencing, nucleic acid quantification, sequencingoptimization, detecting gene expression, quantifying gene expression,genomic profiling, cancer profiling, or analysis of expressed markers.Moreover, the systems and methods have numerous medical applications.For example, it may be used for the identification, detection,diagnosis, treatment, monitoring, staging of, or risk prediction ofvarious genetic and non-genetic diseases and disorders including cancer.It may be used to assess subject response to different treatments of thegenetic and non-genetic diseases, or provide information regardingdisease progression and prognosis.

Computer Control Systems

The present disclosure provides computer control systems that areprogrammed to implement methods of the disclosure. FIG. 1 shows acomputer system 901 that is programmed or otherwise configured toanalyze sequencing data, detect tumor markers and determine cancerstatus. The computer system 901 can regulate various aspects of sequenceanalysis of the present disclosure, such as, for example, matching dataagainst known sequences and variants. The computer system 901 can be anelectronic device of a user or a computer system that is remotelylocated with respect to the electronic device. The electronic device canbe a mobile electronic device.

The computer system 901 includes a central processing unit (CPU, also“processor” and “computer processor” herein) 905, which can be a singlecore or multi core processor, or a plurality of processors for parallelprocessing. The computer system 901 also includes memory or memorylocation 910 (e.g., random-access memory, read-only memory, flashmemory), electronic storage unit 915 (e.g., hard disk), communicationinterface 920 (e.g., network adapter) for communicating with one or moreother systems, and peripheral devices 925, such as cache, other memory,data storage and/or electronic display adapters. The memory 910, storageunit 915, interface 920 and peripheral devices 925 are in communicationwith the CPU 905 through a communication bus (solid lines), such as amotherboard. The storage unit 915 can be a data storage unit (or datarepository) for storing data. The computer system 901 can be operativelycoupled to a computer network (“network”) 930 with the aid of thecommunication interface 920. The network 930 can be the Internet, aninternet and/or extranet, or an intranet and/or extranet that is incommunication with the Internet. The network 930 in some cases is atelecommunication and/or data network. The network 930 can include oneor more computer servers, which can enable distributed computing, suchas cloud computing. The network 930, in some cases with the aid of thecomputer system 901, can implement a peer-to-peer network, which mayenable devices coupled to the computer system 901 to behave as a clientor a server.

The CPU 905 can execute a sequence of machine-readable instructions,which can be embodied in a program or software. The instructions may bestored in a memory location, such as the memory 910. The instructionscan be directed to the CPU 905, which can subsequently program orotherwise configure. The CPU 905 to implement methods of the presentdisclosure. Examples of operations performed by the CPU 905 can includefetch, decode, execute, and writeback.

The CPU 905 can be part of a circuit, such as an integrated circuit. Oneor more other components of the system 901 can be included in thecircuit. In some cases, the circuit is an application specificintegrated circuit (ASIC).

The storage unit 915 can store files, such as drivers, libraries andsaved programs. The storage unit 915 can store user data, e.g., userpreferences and user programs. The computer system 901 in some cases caninclude one or more additional data storage units that are external tothe computer system 901, such as located on a remote server that is incommunication with the computer system 901 through an intranet or theInternet.

The computer system 901 can communicate with one or more remote computersystems through the network 930. For instance, the computer system 901can communicate with a remote computer system of a user (e.g., aphysician). Examples of remote computer systems include personalcomputers (e.g., portable PC), slate or tablet PC's (e.g., Apple® iPad,Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone,Android-enabled device, Blackberry®), or personal digital assistants.The user can access the computer system 901 via the network 930.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 901, such as, for example, on the memory910 or electronic storage unit 915. The machine executable or machinereadable code can be provided in the form of software. During use, thecode can be executed by the processor 905. In some cases, the code canbe retrieved from the storage unit 915 and stored on the memory 910 forready access by the processor 905. In some situations, the electronicstorage unit 915 can be precluded, and machine-executable instructionsare stored on memory 910.

The code can be pre-compiled and configured for use with a machinehaving a processor adapted to execute the code, or can be compiledduring runtime. The code can be supplied in a programming language thatcan be selected to enable the code to execute in a pre-compiled oras-compiled fashion.

Aspects of the systems and methods provided herein, such as the computersystem 901, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such as memory (e.g., read-only memory, random-accessmemory, flash memory) or a hard disk. “Storage” type media can includeany or all of the tangible memory of the computers, processors or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providenon-transitory storage at any time for the software programming. All orportions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, another type of media that may bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to non-transitory, tangible “storage”media, terms such as computer or machine “readable medium” refer to anymedium that participates in providing instructions to a processor forexecution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 901 can include or be in communication with anelectronic display 935 that comprises a user interface (UI) 940 forproviding, for example, information about cancer diagnosis. Examples ofUI's include, without limitation, a graphical user interface (GUI) andweb-based user interface.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms. An algorithm can be implemented by way ofsoftware upon execution by the central processing unit 905. Thealgorithm can, for example, determine whether a cancer is present and/orprogressing.

EXAMPLES Example 1: Next Generation Sequencing Assay for Detection ofctDNA in Early Stage Cancer Patients

A 12 kb gene panel was applied to a clinical study involving 20 earlystage (II/III) and 1 stage IV CRC patients with both pre- andintra-op/follow-up blood draws at two sites, University of California,San Francisco (UCSF) and Samsung Medical Center (SMC). A subset (12patients) also had tumor samples collected at the time of the surgery.FIG. 1 depicts an example of the study design.

FIG. 2 depicts the experimental procedure. Cell-free tumor DNA, (ctDNA)was isolated from plasma. Plasma samples were less than 10 mL andyielded 10-300 ng of cfDNA, corresponding to 3000-91000 mol/base. Atotal of 24 “non-unique” DNA tags were ligated to the cfDNA fragments.DNA fragments corresponding to a 12 kb gene panel were captured usingbiotinylated 120-mer RNA nucleotides and sequenced at 120,000× depth.Noise filtering and molecular tracking were applied and variants werecalled for SNVs and indels. FIG. 3 shows the composition of the 12 kbgene panel.

Overall, driver mutations were detected in 75% of the pre-op plasma ofpatients with matched tumor (9/12). FIG. 5 shows all patients withdetected SNVs with a minor allele frequency (MAF) >0.02%. The detectionrate of ctDNA in pre-op blood draws was 86% (18/21). In theintra-op/follow-up blood draws, ctDNA was detectable in 48% of cases. Inthe samples with no tissues as a reference, mutations detected pre-opwere also observed in follow up blood draws for 25% of patients (2/8).The estimated average minor allele frequency (MAF) is 0.50% (±0.79%) inpre-op, 0.17% (±0.18%) in intra-op/follow-up, and 34% (±17%) in tumorsamples. When tumor tissue was available and used as a reference, theclinical sensitivity, specificity, and accuracy in pre-op blood sampleswere 57%, 99.997%, and 99.99%, respectively. SNVs with MAF as low as0.03% were confirmed in tissue data. The clinical specificity ofvariants detected in intra-op/follow-up blood samples using pre-opsamples as the reference is 99.996%. Specificity across a screen of 26healthy individuals is 99.9987%.

Detection rates were calculated using surgically resected tumor orpre-operation blood samples, in cases were tumor tissue was notavailable, as a reference, see FIG. 6. A driver mutation also found intumor was detected pre-op in 9 of the 12 patients with matched tumor,and in 6 of 9 stage II patients. A driver mutation was detected intra-opin 3 of the 6 patients with an intra-op blood draw and 1 of the 3 stageII patients with an intra-op blood draw. A driver mutation was detectedin 2 of the 6 patients where follow-up blood draws were taken (all stageII). In follow up blood draws, 31% of stage II patients had an initialdriver mutation detected, indicating incomplete resection of the tumor.

FIG. 7 shows concordance analysis for SNV results for pre-op blood drawsusing tumor NGS on surgically resected tumor as reference. For overall,all reported SNVs from 12 samples are considered, while for stage IIonly, all reported SNVs for 9 samples are considered.

FIG. 8 shows key sample preparation values. The yields from extractingpost-op/follow-up plasma are 2.4× pre-op yields despite comparableplasma volumes. UCSF post-op blood draws were taken immediatelyfollowing surgery, while SMC follow-up blood draws were taken 7 daysafter surgery. A0097 and A0105 have two post/follow-up values because a3-month and 24-day follow-up, respectively, were taken for thesepatients.

Example 2: Early, Molecular Detection of Cancer Utilizing CirculatingCell-Free DNA Assay with Ultra-High Accuracy and Sensitivity

This example demonstrates a study with Colorectal Cancer (CRC) patientsand the subsequent assay improvements driven by the results. In thisexample, earlier stage cancers, with which less tumor DNA was shed intothe circulation (FIG. 11), and was detected by the analysis of cell-freecirculating tumor DNA (ctDNA) with next-generation sequencing (NGS).

Methods

As a first iteration of this process, a 25 kb capture panel wasdeveloped based on the landscape of genomic alterations in ctDNA of over10,000 advanced cancer patients (GH database). The panel content wasselected to achieve high clinical sensitivities for colorectal (96%),ovarian (95%), lung (87-93%), and pancreatic (88%).

Panel: The 25-gene panel reported SNVs in 25 genes, indels in 7 genes,and fusions in 1 gene. FIG. 12 shows oncoprints of four major cancertypes: colorectal adenocarcinoma, pancreatic adenocarcinoma, lungadenocarcinoma, and ovarian serous cystadenocarcinoma corresponding to asubset of genes on the 25-gene panel.

Feasibility Study Design: This panel was applied to a study cohort of 21CRC patients with both pre- and intra-op/follow-up blood draws at twosites, UCSF and SMC. A subset (12 patients) also had tumor specimenscollected at the time of the surgery provided for sequencing. A pipelineof the study design is described in FIG. 13.

Results

Overall, mutations were detected in 86% (19/21) of all pre-op samplesand 75% (9/12) of those with matched tumor. Index mutations, defined asmutations that are detected in either two or more blood draws or intumor and a single blood draw, persisted in 29% of patientspost-surgery, indicating molecular residual disease. SNVs with mutantallele fraction (MAF) as low as 0.03% cell-free DNA (cfDNA) wereconfirmed in tissue data. The estimated average mutant allele frequency(MAF) was 0.48% (±0.76%) in pre-op, and 0.16% (±0.17%) inintra-op/follow-up. When tumor tissue was available and used as areference, the clinical sensitivity in pre-op blood samples was 57%, andthe positive predictive value was 75%, perhaps reflecting tissue-basedfalse negatives. Specificity across a screen of 26 healthy individualswas 99.9987%. Cohort expansion to 50 patients and follow-up for clinicalrecurrence in both cohorts was ongoing, as was expansion to additionalcancer types. FIGS. 14A, 14B, 14C and 14D show a time courses for fourpatients. All reported tumor-positive SNVs and insertions/deletions wereincluded. FIGS. 14A and 14B demonstrate that surgery successfullyremoved key mutations. FIGS. 14C and 14D show evidence of molecularresidual disease.

Tables 1 and 2 show the reported mutations. Table 1 shows reported SNVsand indels for tumor-positive mutations. SNVs and indels with MAF >0.02%were reported. Dash indicates that the mutation was not detected.Parentheses indicate a mutation that our pipeline detected but did nothave sufficient support to call. Table 2 shows reported SNVs and indelsfor samples without matched tumor. SNVs and indels with MAF >0.02% werereported. Dash indicates that the mutation was not detected.

TABLE 1 Clin- Patho- MAF (%) ical logical Pre Intra op/ Patient StageStage Gene Mutation op Follow up SMC2016_1 II — KRAS G13D — 0.06 APCp.Leu1489fs — — TP53 p.Pro47fs — — SMC2016_2 II — KRAS G12V — — TP53C141Y — — SMC2016_3 II — KRAS G12V 0.12 0.19 TP53 Y126C 0.14 SMC2016_4II — TP53 P278S 0.03 — APC p.Arg216fs — — SMC2016_5 II — APC R1386* — —TP53 P152L — — SMC2016_6 II — TP53 L252P 0.04 — APC K523* — — KRAS G12D— — A0097 II III APC Q1378 2.86 0.17 TP53 R213* 3.47 0.20 A0098 II IIIAPC R283* 0.2  (0.03) APC F1396F 0.3  — KRAS G12V 0.5  — APC p.Gln1406fs0.2  — TP53 p.Asn239fs — — A0101 II II TP53 W91* 0.07 — APC p.Leu620fs —— A0102 IV IV KRAS G12V 0.27 0.07 TP53 p.Asn239dup 0.25 0.09 A0105 II IITP53 R273C 0.14 (0.01) KRAS G12D — — A0106 II II TP53 H214R 0.28 0.04APC S1356* 0.29 — KRAS G12V — —

TABLE 2 MAF (%) Clinical Pathological Pre Intra op/ Patient Stage StageGene Mutation op Follow up A111 II — TP53 R158H 0.38 0.40 NRAS E76K —0.03 1 SMC II — APC E1306* 0.4 — TP53 G245S 0.5 — KRAS D173D 2.1 — 2 SMCII — TP53 R273H 0.07 — 3 SMC II — TP53 G245D 0.4 0.3  TP53 C242Y 0.60.8  KRAS G12D 0.2 — 4 SMC II — APC E1397* 0.2 — APC R213* 0.3 — TP53H179R 1.1 — 5 SMC II — APC p.Lys1616_Leu1617delinsAsn — 0.11 6 SMC II —TP53 R282Q 0.49 — TP53 T125M 0.34 — 7 SMC II — APC S2586D 0.17 0.15 APCp.Arg1048fs 0.08 0.07 8 SMC II — KRAS G12D 0.25 —

Table 3 shows the assay metrics and performance values. The yields fromextracting intra-op/follow-up plasma were 2.4× pre-op yields despitecomparable plasma volumes. UCSF intra-op blood draws were takenimmediately following surgery, while SMC follow-up blood draws weretaken 7 days after surgery.

TABLE 3 Total cfDNA Plasma (mL) Yield (ng/mL) Library Input (ng) Intraop/ Intra op/ Intra op/ Pre Follow up Pre op Follow up Pre op Follow up4.9 4.4 10 24 40 100

Table 4 shows the detection rate by patients. A mutation was detectedpre-op in 9 of 12 patients with matched tumor. A mutation was detectedintra-op in 4 of the 7 patients with an intra-op blood draw. A mutationwas detected at follow-up in 4/14 patients where follow-up blood drawswere taken. If mutations with any evidence were included, the percentageof patients with a mutation detected intra-op is 86% overall.

TABLE 4 % of patients with mutation detected pre op 75% intra op 57%follow up 29%

Assay improvements for increased sensitivity: The CRC-specific panel wasexpanded into a new panel designed to achieve high clinical sensitivityfor colorectal, ovarian, lung, and pancreatic cancers. Sites wheremutations were prevalent in the GH database were also included. This25-gene panel reported SNVs in 25 genes, indels in 7 genes, and fusionsin 1 gene. FIG. 15 shows genes selected for detection of major cancertypes with >90% theoretical sensitivity. Bolded genes indicate geneswith complete exon coverage. FIGS. 16A and 16B show improved diversityand gene coverage to achieve greater sensitivity. FIG. 16A showsdiversity across cfDNA input for analytical samples. Molecularconversions did not reach saturation over our range of cfDNA input.Higher diversity allows for detection at lower MAFs. FIG. 16B shows twokey genes with significant coverage improvements with assayoptimization.

Conclusion

A non-invasive multigene cfDNA NGS assay was developed in this examplefor the early detection of ctDNA. In this example, the early-stage assaydetected ctDNA alterations present in the post-surgical tumor specimenin 75% of patients' pre-op samples and at mutant allele fractions anorder of magnitude lower than the current clinical assay. Evidence ofmolecular residual disease was found in 29% of patients regardless ofstage and follow-up to correlated MRD with clinical outcomes.

Example 3: Detection of Lung Cancer in High Risk Smokers with IrregularNodules Using a Circulating Cell-Free DNA Assay with Ultra-High Accuracyand Specificity

In about 40 percent of cases, non-small cell lung cancer (NSCLC) may bediagnosed in stage 4, at which point a diagnosed patient may have lessthan a year of expected survival time. Liquid biopsies can aid inearlier detection of lung cancer among a cohort of patients (e.g.,smokers) at high risk for lung cancer (e.g., higher than average riskcompared to the general population) with irregular nodules. Sinceroughly half of smokers over the age of 50 may be expected to presentwith one or more lung nodules on a CT chest scan, a highly sensitive andspecific non-invasive test (e.g., a circulating cfDNA assay) may beneeded to perform differential diagnosis of lung cancer. With earlierstage cancers, less tumor DNA is shed into circulation, thus requiringvery high sensitivity. The specificity of the circulating cfDNA assay ona subject may requirement enhancement because subjects in a high risksmoker cohort may be expected to exhibit a high prevalence of genomicalterations not necessarily associated with cancer. Thus, a diagnosis oflung cancer by liquid biopsy may be enhanced by incorporation ofadditional clinical information (e.g., age, smoking history, andradiological data) of the subject. In addition, specificity can befurther enhanced by performing validation testing on a lung nodule afterit is removed. A clinical decision to remove such a lung nodule may beperformed based in part on the results of the circulating cfDNA assay.

A 7.5 kb ctDNA capture panel is developed based on the landscape ofgenomic alterations in ctDNA of 10,000 advanced lung cancer patientswith high theoretical clinical sensitivity for lung cancer (87-93%). Thepanel is used in a cfDNA assay to achieve a PPV of 95% at 0.025%-0.05%MAF and a PPV of 99% above 0.05% MAF. The panel is applied to a clinicalstudy of 100 high-risk subjects with significant history of smokinghaving irregular nodules of indeterminate status (e.g., no definitiveclinical diagnosis of a benign or malignant tumor in the lung). Thedetection rate of ctDNA in blood draws from the subjects is 40%(40/100). Based on the identification of ctDNA, the 40 positivelyidentified subjects are further subjected to surgical removal andsequencing analysis of their lung nodules. Sequencing analysis of thelung nodules confirms a diagnosis of lung cancer in 90% (36/40) of thesubjects with a positive ctDNA test. The remaining 60 subjects who didnot exhibit detectable ctDNA are subjected to repeated cfDNA testingevery month, and subjects who subsequently receive a positiveidentification of ctDNA have a likely diagnosis of lung cancer, whichcan be confirmed by surgical removal and sequencing analysis of lungnodules. If the cohort of 100 high-risk subjects with significanthistory of smoking having irregular nodules of indeterminate status didnot receive a ctDNA assay, the subjects may receive painful biopsiesand/or follow-up radiological scans on 6 to 12 month intervals toobserve any clinical changes (e.g., nodule growth) to obtain moredefinitive diagnoses of lung cancer.

In conclusion, a clinically useful assay is developed for the detectionof ctDNA in a cohort of high-risk subjects with irregular lung nodules.This allows for a non-invasive route for high sensitivity andspecificity diagnosis of lung cancer compared to traditional clinicalmethodologies.

Example 4: Assaying ctDNA Utilizing a High-Sensitivity Panel Detects aHigh-Level MET Amplification in Lung Cancer and Guides Therapy Selection

A 70-year-old former light smoker (15 packs/year) with pulmonaryfibrosis and moderate pulmonary hypertension was diagnosed with a 30 mmright middle lobe stage IIIA lung adenocarcinoma and treated withdefinitive chemoradiotherapy. After five months, mediastinal, liver, andmultiple bone metastases were diagnosed. After two months of treatmentwith a targeted therapeutic regimen (afatinib) for a rare EGFR mutation(I744F), a significant progression occurred. The patient was not acandidate for chemotherapy and there was no tissue available formolecular testing.

Circulating tumor DNA (CtDNA) testing was performed with a 70-gene ctDNANGS panel (see Table 5) that includes all NCCN-recommended somaticgenomic variants for solid tumors and completely sequences the criticalexons in 70 genes to identify all four major types of genomicalterations: single nucleotide variants (SNVs), selected indels andfusions, and copy number amplifications (CNA) in 16 genes with highsensitivity (85% in stage III/IV solid tumors) and ultra-highspecificity (>99.9999%). CNA for MET and other genes have been validatedagainst cell lines with known amplifications and are reported as 1+, 2+or 3+ with the latter representing the absolute copy number of the genein blood at the 90th percentile and higher.

TABLE 5 POINT MUTATIONS - Complete* or Critical Exon Coverage in 70Genes AKT1 ALK APC AR ARAF ARID1A ATM BRAF BRCA1 BRCA2 CCDN1 CCND2 CCNE1CDH1 CDK4 CDK6 CDKN2A CDKN2B CTNNB1 EGFR ERBB2 ESR1 EZH2 FBXW7 FGFR1FGFR2 FGFR3 GATA3 GNA11 GNAQ GNAS HNF1A HRAS IDH1 IDH2 JAK2 JAK3 KITKRAS MAP2K1 MAP2K2 MET MLH1 MPL MYC NF1 NFE2L2 NOTCH1 NPM1 NRAS NTRK1PDGFRA PIK3CA PTEN PTPN11 RAF1 RB1 RET RHEB RHOA RIT1 ROS1 SMAD4 SMO SRCSTK11 TERT TP53 TSC1 VHL AMPLIFICATIONS AR BRAF CCNE1 CDK4 CDK6 EGFRERBB2 FGFR1 FGFR2 KIT KRAS MET MYC PDGFRA PIK3CA RAF1 FUSIONS ALK FGFR2FGFR3 RET ROS1 NTRK1 INDELS EGFR exons 19/20 ERBB2 exons 19/20 MET exon14 skipping

CtDNA NGS testing identified a high-level MET amplification (copy numberof 53.6 in circulation) (FIG. 17A). The test was repeated on a secondtube of blood submitted at the same time point, with the second testshowing a similar MET gene copy number (60.0). Crizotinib was prescribedto target the MET amplification. After treatment of the patient wasstarted, comprising administering the anti-MET therapy to the subject totreat the NSCLC, immediate clinical improvement and a significantimaging response on CT/PET scans were observed (FIG. 17B). Three monthsafter start of treatment, the patient was fully active, able to carry onall predisease performance without restriction (ECOG PerformanceStatus=0) and was symptom-free. Similar ctDNA testing on other NSCLCpatients may yield an identification of a CNA of the MET gene in thectDNA of at least about 20, at least about 30, at least about 40, or atleast about 50. The CNA may be identified with a sensitivity of at least80%. The CNA may be identified with a specificity of at least 99.9%, atleast 99.99%, at least 99.999%, or at least 99.9999%.

Analysis of ctDNA in this metastatic NSCLC cancer patient identified METgene amplification and the patient had a dramatic response tocrizotinib. Liquid biopsy methods such as ddPCR may identify EGFR T790M,but NGS methods may be required to detect the other 50% of the secondaryresistance mechanisms (FIG. 18), such as MET amplification—which occursin 5% of patients on EGFR inhibitors. CtDNA detection of METamplification as a key resistance mechanism after EGFR TKI therapy isfeasible with a targeted NGS method when tissue is not accessible orbiopsy performed but was quantity not sufficient (QNS) for genotyping.

Example 5: Assaying ctDNA Utilizing a High-Sensitivity Panel DetectsERBB2 Mutations (e.g., Indels) in Breast Cancer and Guides TherapySelection

Two percent of metastatic breast cancer (MBC), predominantly HER2non-amplified patients, harbor ERBB2 (HER2) single nucleotide variantsor indels which may benefit from targeted tyrosine kinase inhibitors.ERBB2 mutations may be non-invasively identified and intreatment-refractory MBC with targeted next generation sequencing (NGS)of circulating tumor DNA (ctDNA).

Serial ctDNA testing was performed at the time of initial metastaticdiagnosis and at each progression with a 70-gene ctDNA NGS panel (seeTable 5) that includes all NCCN-recommended somatic genomic variants forsolid tumors and sequences complete exons of 70 genes to report singlenucleotide variants (SNVs), fusions, amplifications, and indels withhigh sensitivity (85% in stage III/IV solid tumors) and ultra-highspecificity (>99.9999%). CT scans of the chest and abdomen wereperformed and correlated with ctDNA levels (FIG. 19).

The patient's initial blood draw detected the ERBB2 exon 19 indelp.Leu755_Glu757delinsSer with a mutant allele fraction of 9.0%. CT scanof the liver from October 2014 demonstrated moderate tumor burden in theliver. Based on identifying the mutation in the ctDNA, a treatment wasidentified to be administered to the subject to treat the breast cancer,and the treatment was administered to the subject to treat the breastcancer. After an initial molecular response to combined trastuzumab,pertuzumab and docetaxel, molecular evidence of tumor progression waspresent on the February 2015 ctDNA assay. The patient's tumor continuedto progress clinically in the liver, as demonstrated by the CT scan fromMay 2015. ctDNA analysis in September 2015 showed drastic reduction inctDNA levels, with all mutations dropping <1.0% mutant allele fraction.This molecular response correlated with marked reduction of disease inthe liver as shown on the October 2015 CT scan. One or more mutations inthe ctDNA from the subject may be identified with a sensitivity of atleast 80%. One or more mutations in the ctDNA from the subject may beidentified with a specificity of at least 99%, at least 99.9%, at least99.99%, at least 99.999%, or at least 99.9999%.

In another case, a patient was initially diagnosed with ER/PR positiveinvasive breast cancer at age 44 and was treated with surgery, followedby hormonal therapy after a local recurrence. At age 61, she was foundto have axillary adenopathy and liver metastases. Treatment details andthe patient's clinical status following the diagnosis of MBC are shownin FIG. 19.

Analysis of ctDNA in this metastatic breast cancer patient identified anin-frame activating ERBB2 insertion/deletion in exon 19, analogous toEGFR activating mutations in lung adenocarcinoma. There was a molecularresponse to anti-HER2 therapy initially with the ERBB2 indel droppingfrom 9.0% to 1.0% mutant allele fraction. It is presumed that emergingclones acquired resistance mechanisms besides the ERBB2 indel, and droveprogression.

Serial monitoring of ctDNA reflected clinical and radiographicprogression and response to subsequent lines of chemotherapy. Knowingthe specific ERBB2 variant is important, as specific ERBB2 variants maydrive sensitivity or resistance to differing anti-HER2 monoclonalantibody or dual anti-EGFR/ERBB2 tyrosine kinase inhibitor therapies. Inconclusion, NCCN guidelines should include recommendations for ctDNA NGSfor treatment-refractory MBC patients to identify actionable ERBB2mutations as is recommended for metastatic non-small cell lung cancer.

Example 6: Assaying ctDNA Utilizing a High-Sensitivity Panel DetectsERBB2 Mutations (e.g., Indels) in Lung Cancer and Guides TherapySelection

Genotyping of metastatic non-small cell lung cancer (NSCLC) has becomestandard of care, targeting the canonical driver mutations in sevengenes: EGFR, BRAF, MET and fusions in ALK, RET, and ROS 1 and “HER2[ERBB2 gene] mutations”. Unlike the EGFR gene where mutation andamplification co-occur 80% of the time, ERBB2 indels and singlenucleotide variants (SNVs) are generally mutually exclusive from ERBB2gene amplification and suggest different treatments.

ERBB2 in-frame indels between codons 775 and 881 in exon 19 and 20 ofthe ERBB2 gene, of which a 12 base pair exon 20 YVMA insertion is themost common, are activating mutations in 2% of NSCLC, especially lungadenocarcinoma (LUAD). Targeted next generation sequencing (NGS) ofcell-free circulating tumor DNA (ctDNA) provides a non-invasive means ofidentifying these potential ERBB2 driver mutations, especially whentissue biopsies are quantity not sufficient (QNS) for analysis or areundergenotyped.

A single ERBB2 exon 19 deletion p.Arg756 Glu757delinsLys at 3.9% mutantallele fraction (MAF) was noted in one patient, for whom outcome datawas available. Initial tissue was immunohistochemistry (IHC) negativefor HER2 overexpression at the referring hospital where the archivaltissue biopsy was exhausted and thus could not be sequenced. Based onthe cfDNA finding of an ERBB2 indel, the patient was switched fromcytotoxic chemotherapy to trastuzumab with objective response on PET/CTand a repeat Guardant360™ showed the ERBB2 indel MAF had dropped belowthe test limit of detection. After four months the patient's tumorprogressed and the ERBB2 indel MAF rose to 0.4%. It was decided toswitch to ado-emtansine trastuzumab (T-DM1).

Guardant360™ is a targeted cfDNA NGS panel using hybrid capture andcomplete exon sequencing for single nucleotide variant detection in 70genes, copy number amplifications (CNA) in 16 genes, fusions in sixgenes, and small indels in EGFR, ERBB2 and MET exon 14 skipping (seeFIG. 20).

Indels are detected down to 0.04% mutant allele fraction with ultra-highspecificity (>99.998%). CNAs are detected at 99.8% specificity down to2.2 copies. De-identified pathology and genotyping reports were reviewedfor consecutive NSCLC patients in whom ERBB2 indels and geneamplifications were identified in clinical practice.

5,684 consecutive samples from 5,211 unique patients with advanced NSCLCor lung adenocarcinoma were genotyped. 57 unique patients were foundwith ERBB2 indels (1.09%) and 8 (14.04%) of these were also ERBB2amplified (see Table 1). 9 patients had pathology reports withtissue-based NGS results, 7 confirming the ERBB2 indel (78% PPV). Thetwo discordant tissue NGS samples were 10 and 21 months older than theplasma-based test. Known function SNVs are shown in FIG. 2: codons 143and 340 (not shown) were also recurrently mutated; ERBB2 SNVs occurredat 0.5% frequency. One or more ERBB2 indels in the ctDNA from thesubject may be identified with a sensitivity of at least 80%. One ormore ERBB2 indels in the ctDNA from the subject may be identified with aspecificity of at least 99%, at least 99.9%, at least 99.99%, at least99.999%, or at least 99.9999%.

Conclusions: 1) ERBB2 indels were found at a lower frequency (1.1% vs.2%) than reported in the literature, perhaps reflecting that some of theNSCLC patients were not LUAD. 2) ERBB2 indels were not exclusive ofERBB2 amplification, perhaps reflecting this clinical cohort where >80%of patients have progressed on treatment and where copy numberamplification may be a mechanism of treatment resistance. 3) ERBB2indels, SNVs, and CNAs can be identified without tissue biopsy in NSCLCpatients in this large series of over 5,000 patients. 4) In a patientwhose tissue was not available for sequencing, an objective responsewith trastuzumab was obtained for an ERBB2 exon 19 indel.

While embodiments of the present disclosure have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. It is not intendedthat the disclosure be limited by the specific examples provided withinthe specification. While the disclosure has been described withreference to the aforementioned specification, the descriptions andillustrations of the embodiments herein are not meant to be construed ina limiting sense. Numerous variations, changes, and substitutions willnow occur to those skilled in the art without departing from thedisclosure. Furthermore, it shall be understood that all aspects of thedisclosure are not limited to the specific depictions, configurations orrelative proportions set forth herein which depend upon a variety ofconditions and variables. It should be understood that variousalternatives to the embodiments of the disclosure described herein maybe employed in practicing the disclosure. It is therefore contemplatedthat the disclosure shall also cover any such alternatives,modifications, variations or equivalents. It is intended that thefollowing claims define the scope of the disclosure and that methods andstructures within the scope of these claims and their equivalents becovered thereby.

1. A method comprising: (a) providing a sample comprising cell-freenucleic acid (cfNA) molecules from a subject, wherein the subject doesnot detectably exhibit a cancer; (b) capturing from the sample cfNAmolecules covered by a sequencing panel, wherein the sequencing panelcomprises one or more regions from each of a plurality of differentgenes, wherein: i. the sequencing panel is no greater than 50,000nucleotides; ii. a presence of a tumor marker in any one of thedifferent genes indicates that the subject has the cancer; and iii. atleast 80% of subjects having the cancer have a tumor marker present inat least one of the plurality of different genes; and (c) sequencing thecaptured cfNA molecules to a read depth sufficient to detect the tumormarkers at a frequency in the sample as low as 1.0%, 0.75%, 0.5%, 0.25%,0.1%, 0.075%, 0.05%, 0.025%, 0.01%, or 0.005%.
 2. The method of claim 1,wherein the cfNA molecules are derived from blood, serum, or plasma. 3.The method of claim 1, wherein the sample comprises between 10 nanogramsand 300 nanograms of cfNA, and wherein the cfNA is circulating cell-freeDNA (cfDNA).
 4. The method of claim 1, wherein the cancer is selectedfrom the group consisting of ovarian cancer, pancreatic cancer, breastcancer, colorectal cancer, and non-small cell lung carcinoma (NSCLC), ora combination thereof.
 5. The method of claim 1, wherein the cancer isnon-small cell lung carcinoma (NSCLC), and the non-small cell lungcarcinoma (NSCLC) is squamous cell carcinoma or adenocarcinoma.
 6. Themethod of claim 1, wherein the subject does not detectably exhibit thecancer as shown by one or more imaging methods selected from the groupconsisting of positron emission tomography scan, magnetic resonanceimaging, X-ray, computerized axial tomography scan, and ultrasound. 7.The method of claim 1, wherein the subject has previously undergonetreatment for the cancer.
 8. (canceled)
 9. The method of claim 1,wherein the plurality of genes is between 2 to 30 different genes. 10.The method of claim 1, wherein the plurality of genes is no more thanany of 10, 9, 8, 7, 6, or 5 different genes.
 11. The method of claim 1,wherein the panel comprises a plurality of genes selected from the groupconsisting of AKT1, ALK, APC, ATM, BRAF, CTNNB1, EGFR, ERBB2, ESR1,FGFR2, GATA3, GNAS, IDH1, IDH2, KIT, KRAS, MET, NRAS, PDGFRA, PIK3CA,PTEN, RB1, SMAD4, STK11, and TP53.
 12. The method of claim 1, whereinthe sequencing panel is about 15,000 nucleotides to about 30,000nucleotides.
 13. The method of claim 1, wherein the tumor marker isselected from the group consisting of a single base substitution, aninsertion or deletion (indel), a gene fusion, a transversion, atranslocation, an inversion, a deletion, aneuploidy, partial aneuploidy,polyploidy, chromosomal instability, chromosomal structure alterations,chromosome fusions, a gene truncation, a gene amplification, a geneduplication, a chromosomal lesion, a DNA lesion, abnormal changes innucleic acid chemical modifications, abnormal changes in epigeneticpatterns and abnormal changes in nucleic acid methylation.
 14. Themethod of claim 1, wherein at least 85%, at least 90%, at least 93%, atleast 95%, at least 97%, at least 98%, or at least 99% of subjectshaving the cancer have a tumor marker present in at least one of theplurality of different genes.
 15. The method of claim 1, comprisingsequencing the captured cfNA molecules to a read depth sufficient todetect the tumor markers at a frequency in the sample as low as 0.005%,0.001%, or 0.0005%.
 16. The method of claim 1, wherein the one or moreregions are selected for the sequencing panel to detect one or moredifferentially methylated regions.
 17. The method of claim 1, whereinthe one or more regions comprise sequences differentially transcribedacross one or more tissues of the subject.
 18. The method of claim 1,wherein the sequencing panel is selected to detect the one or more tumormarkers with a theoretical sensitivity of 85% or greater.
 19. The methodof claim 1, wherein the panel is selected to achieve a sensitivity of85% or greater for one or more cancers selected from the groupconsisting of colorectal cancer, ovarian cancer, lung cancer, andpancreatic cancer.
 20. The method of claim 1, wherein assaying the cfNAmolecules comprises subjecting the cfNA molecules to sequencing in theone or more regions in the sequencing panel to generate sequence reads.21-87. (canceled)
 88. A method for identifying treatment for a subjectwith non-small cell lung carcinoma (NSCLC), comprising: (a) sequencingcell-free DNA (cfDNA) molecules derived from a cell-free DNA (cfDNA)sample obtained from the subject; (b) analyzing sequence reads derivedfrom the sequencing to identify (i) circulating tumor DNA (ctDNA) amongthe cfDNA molecules and (ii) a copy number amplification (CNA) of theMET gene in the ctDNA with a specificity of at least 99%; and (c)identifying, based at least on the identified CNA of the MET gene, ananti-MET therapy to be administered to the subject to treat the NSCLC.89-102. (canceled)
 103. A method for monitoring breast cancer in asubject, comprising: (a) sequencing cell-free DNA (cfDNA) moleculesderived from a cell-free DNA (cfDNA) sample obtained from the subject;and (b) analyzing sequence reads derived from the sequencing to identify(i) circulating tumor DNA (ctDNA) among the cfDNA molecules and (ii) oneor more mutations in the ctDNA from the subject selected from: EGFR,Exon 19 deletion; TP53, E286K mutation; AR, N706S mutation; ALK, G1137Rmutation; MAP2K2, E66K mutation; and TP53, K164E mutation. 104-136.(canceled)